2018 |
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article Artificial Intelligence, 262 , pp. 248–278, 2018. @article{soton422097, title = {Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes}, author = {Emmanouil Rigas and Sarvapali Ramchurn and Nick Bassiliades}, url = {https://eprints.soton.ac.uk/422097/}, year = {2018}, date = {2018-09-01}, journal = {Artificial Intelligence}, volume = {262}, pages = {248--278}, abstract = {We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pick-up and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is used. When some EVs do not have a large enough battery to execute some of the longest trips, the incremental-MIP generates solutions slightly better than the greedy, while the optimal algorithm is the best but scales up to medium sized problems only. Moreover, the online algorithm is shown to be on average at least 90% of the optimal. Finally, the greedy algorithm scales to 10-times more tasks than the incremental-MIP and 1000-times more than the static MIP in reasonable time.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pick-up and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is used. When some EVs do not have a large enough battery to execute some of the longest trips, the incremental-MIP generates solutions slightly better than the greedy, while the optimal algorithm is the best but scales up to medium sized problems only. Moreover, the online algorithm is shown to be on average at least 90% of the optimal. Finally, the greedy algorithm scales to 10-times more tasks than the incremental-MIP and 1000-times more than the static MIP in reasonable time. |
Ayodeji, Opeyemi Abioye; Prior, Stephen; Thomas, Trevor; Saddington, Peter; Ramchurn, Sarvapali D The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot Inproceedings 19th Towards Autonomous Robotic Systems (TAROS) Conference 2018, pp. 423–437, Springer, 2018. @inproceedings{soton418869, title = {The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot}, author = {Opeyemi Abioye Ayodeji and Stephen Prior and Trevor Thomas and Peter Saddington and Sarvapali D. Ramchurn}, url = {https://eprints.soton.ac.uk/418869/}, year = {2018}, date = {2018-07-01}, booktitle = {19th Towards Autonomous Robotic Systems (TAROS) Conference 2018}, volume = {10965}, pages = {423--437}, publisher = {Springer}, abstract = {This paper describes a model of the multimodal speech and visual gesture (mSVG) control for aerobots operating at higher nCA autonomy levels, within the context of a patrol, search, and rescue application. The developed mSVG control architecture, its mathematical navigation model, and some high level command operation models were discussed. This was successfully tested using both MATLAB simulation and python based ROS Gazebo UAV simulations. Some limitations were identified, which formed the basis for the further works presented.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper describes a model of the multimodal speech and visual gesture (mSVG) control for aerobots operating at higher nCA autonomy levels, within the context of a patrol, search, and rescue application. The developed mSVG control architecture, its mathematical navigation model, and some high level command operation models were discussed. This was successfully tested using both MATLAB simulation and python based ROS Gazebo UAV simulations. Some limitations were identified, which formed the basis for the further works presented. |
Ortega, Andre P; Merrett, Geoff; Ramchurn, Sarvapali Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks Inproceedings 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18), 2018. @inproceedings{soton423060, title = {Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks}, author = {Andre P Ortega and Geoff Merrett and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/423060/}, year = {2018}, date = {2018-07-01}, booktitle = {2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18)}, abstract = {As the Internet of Things grows, the number of wireless sensor networks deployed in close proximity will continue to increase. By nature, these networks are limited by the battery supply that determines their lifetime and system utility. To counter such a shortcoming, energy harvesting technologies have become increasingly investigated to provide a perpetual energy source; however, new problems arise as a result of their wide spatio-temporal variation. In this paper, we propose opportunistic energy trading, which enables otherwise independent networks to be sustained by sharing resources. Our goal is to provide a novel cooperation model based on negotiation to solve coordination conflicts between energy harvesting wireless sensor networks. Results show that networks are able to satisfy their loads when they agree to cooperate.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } As the Internet of Things grows, the number of wireless sensor networks deployed in close proximity will continue to increase. By nature, these networks are limited by the battery supply that determines their lifetime and system utility. To counter such a shortcoming, energy harvesting technologies have become increasingly investigated to provide a perpetual energy source; however, new problems arise as a result of their wide spatio-temporal variation. In this paper, we propose opportunistic energy trading, which enables otherwise independent networks to be sustained by sharing resources. Our goal is to provide a novel cooperation model based on negotiation to solve coordination conflicts between energy harvesting wireless sensor networks. Results show that networks are able to satisfy their loads when they agree to cooperate. |
Ayodeji, Opeyemi Abioye; Prior, Stephen; Thomas, Trevor; Saddington, Peter; Ramchurn, Sarvapali IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018, pp. 842–845, IEEE, 2018. @inproceedings{soton418871, title = {Quantifying the effects of varying light-visibility and noise-sound levels in practical multimodal speech and visual gesture (mSVG) interaction with aerobots}, author = {Opeyemi Abioye Ayodeji and Stephen Prior and Trevor Thomas and Peter Saddington and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/418871/}, year = {2018}, date = {2018-06-01}, booktitle = {IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018}, pages = {842--845}, publisher = {IEEE}, abstract = {This paper discusses the research work conducted to quantify the effective range of lighting levels and ambient noise levels in order to inform the design and development of a multimodal speech and visual gesture (mSVG) control interface for the control of a UAV. Noise level variation from 55 dB to 85 dB is observed under control lab conditions to determine where speech commands for a UAV fails, and to consider why, and possibly suggest a solution around this. Similarly, lighting levels are varied within the control lab condition to determine a range of effective visibility levels. The limitation of this work and some further work from this were also presented.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper discusses the research work conducted to quantify the effective range of lighting levels and ambient noise levels in order to inform the design and development of a multimodal speech and visual gesture (mSVG) control interface for the control of a UAV. Noise level variation from 55 dB to 85 dB is observed under control lab conditions to determine where speech commands for a UAV fails, and to consider why, and possibly suggest a solution around this. Similarly, lighting levels are varied within the control lab condition to determine a range of effective visibility levels. The limitation of this work and some further work from this were also presented. |
Garcia, Pedro Garcia; Costanza, Enrico; Verame, Jhim; Nowacka, Diana; Ramchurn, Sarvapali D Seeing (movement) is believing: the effect of motion on perception of automatic systems performance Journal Article Human-Computer Interaction, pp. 1-51, 2018. @article{soton422967, title = {Seeing (movement) is believing: the effect of motion on perception of automatic systems performance}, author = {Pedro Garcia Garcia and Enrico Costanza and Jhim Verame and Diana Nowacka and Sarvapali D Ramchurn}, url = {https://eprints.soton.ac.uk/422967/}, year = {2018}, date = {2018-04-01}, journal = {Human-Computer Interaction}, pages = {1-51}, abstract = {ensuremath keywords = {}, pubstate = {published}, tppubtype = {article} } ensuremath<pensuremath>In this article, we report on one lab study and seven follow-up studies on a crowdsourcing platform designed to investigate the potential of animation cues to influence users? perception of two smart systems: a handwriting recognition and a part-of-speech tagging system. Results from the first three studies indicate that animation cues can influence a participant?s perception of both systems? performance. The subsequent three studies, designed to try and identify an explanation for this effect, suggest that this effect is related to the participants? mental model of the smart system. The last two studies were designed to characterize the effect more in detail, and they revealed that different amounts of animation do not seem to create substantial differences and that the effect persists even when the system?s performance decreases, but only when the difference in performance level between the systems being compared is small.ensuremath</pensuremath> |
Bicego, M; Farinelli, A; Grosso, E; Paolini, D; Ramchurn, S D On the distinctiveness of the electricity load profile Journal Article Pattern Recognition, 74 (Supplement C), pp. 317–325, 2018, ISSN: 0031-3203. @article{BICEGO2018317b, title = {On the distinctiveness of the electricity load profile}, author = {M. Bicego and A. Farinelli and E. Grosso and D. Paolini and S.D. Ramchurn}, url = {http://www.sciencedirect.com/science/article/pii/S0031320317303904}, doi = {https://doi.org/10.1016/j.patcog.2017.09.039}, issn = {0031-3203}, year = {2018}, date = {2018-01-01}, journal = {Pattern Recognition}, volume = {74}, number = {Supplement C}, pages = {317--325}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Olabambo, Ifeoluwa Oluwasuji; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Algorithms for fair load shedding in developing countries Inproceedings Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1590-1596, 2018. @inproceedings{soton420541, title = {Algorithms for fair load shedding in developing countries}, author = {Ifeoluwa Oluwasuji Olabambo and Obaid Malik and Jie Zhang and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/420541/}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {1590-1596}, abstract = {Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. During load shedding, many homes are left without electricity, causing them inconvenience and discomfort. In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity. We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. During load shedding, many homes are left without electricity, causing them inconvenience and discomfort. In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity. We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes. |
Olabambo, Ifeoluwa Oluwasuji; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Algorithms to manage load shedding events in developing countries Inproceedings Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS), pp. 2034-2036, 2018. @inproceedings{soton420127, title = {Algorithms to manage load shedding events in developing countries}, author = {Ifeoluwa Oluwasuji Olabambo and Obaid Malik and Jie Zhang and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/420127/}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS)}, pages = {2034-2036}, abstract = {Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves homes without electricity, causing them discomfort and inconvenience. Because fairness is not a priority when shedding load, some homes bear the brunt of these effects. In this paper, we present our ongoing research into considering fairness when shedding load at the household level.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves homes without electricity, causing them discomfort and inconvenience. Because fairness is not a priority when shedding load, some homes bear the brunt of these effects. In this paper, we present our ongoing research into considering fairness when shedding load at the household level. |
Kiel, Manzano Verame Jhim; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Crabtree, Andy; Rodden, Tom; Jennings, Nick Learning from the veg box: Designing unpredictability in agency delegation Inproceedings CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018. @inproceedings{soton417370, title = {Learning from the veg box: Designing unpredictability in agency delegation}, author = {Manzano Verame Jhim Kiel and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Andy Crabtree and Tom Rodden and Nick Jennings}, url = {https://eprints.soton.ac.uk/417370/}, year = {2018}, date = {2018-01-01}, booktitle = {CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems}, publisher = {ACM}, abstract = {The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semistructured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people?s diverse values, and their coordinative and creative practices.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semistructured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people?s diverse values, and their coordinative and creative practices. |
Vu, Huan; Aknine, Samir; Ramchurn, Sarvapali D A Decentralised Approach to Intersection Traffic Management Inproceedings Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden., pp. 527–533, 2018. @inproceedings{DBLP:conf/ijcai/VuAR18, title = {A Decentralised Approach to Intersection Traffic Management}, author = {Huan Vu and Samir Aknine and Sarvapali D Ramchurn}, url = {https://doi.org/10.24963/ijcai.2018/73}, doi = {10.24963/ijcai.2018/73}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.}, pages = {527--533}, crossref = {DBLP:conf/ijcai/2018}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Khan, Md. Mosaddek; -, Long Tran; Ramchurn, Sarvapali D; Jennings, Nicholas R Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs Journal Article Comput. J., 61 (11), pp. 1639–1666, 2018. @article{DBLP:journals/cj/KhanTRJ18, title = {Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs}, author = {Md. Mosaddek Khan and Long Tran - and Sarvapali D Ramchurn and Nicholas R Jennings}, url = {https://doi.org/10.1093/comjnl/bxy021}, doi = {10.1093/comjnl/bxy021}, year = {2018}, date = {2018-01-01}, journal = {Comput. J.}, volume = {61}, number = {11}, pages = {1639--1666}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Khan, Md. Mosaddek; -, Long Tran; Yeoh, William; Jennings, Nicholas R A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent Systems Inproceedings Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, pp. 1604–1612, 2018. @inproceedings{DBLP:conf/atal/KhanTYJ18, title = {A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent Systems}, author = {Md. Mosaddek Khan and Long Tran - and William Yeoh and Nicholas R Jennings}, url = {http://dl.acm.org/citation.cfm?id=3237938}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018}, pages = {1604--1612}, crossref = {DBLP:conf/atal/2018}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rigas, Emmanouil S; Karapostolakis, Sotiris; Bassiliades, Nick; Ramchurn, Sarvapali D EVLibSim: A tool for the simulation of electric vehicles' charging stations using the EVLib library Journal Article Simulation Modelling Practice and Theory, 87 , pp. 99–119, 2018. @article{DBLP:journals/simpra/RigasKBR18, title = {EVLibSim: A tool for the simulation of electric vehicles' charging stations using the EVLib library}, author = {Emmanouil S Rigas and Sotiris Karapostolakis and Nick Bassiliades and Sarvapali D Ramchurn}, url = {https://doi.org/10.1016/j.simpat.2018.06.007}, doi = {10.1016/j.simpat.2018.06.007}, year = {2018}, date = {2018-01-01}, journal = {Simulation Modelling Practice and Theory}, volume = {87}, pages = {99--119}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2017 |
Balsamo, Domenico; Merrett, Geoff V; Zaghari, Bahareh; Wei, Yang; Ramchurn, Sarvapali; Stein, Sebastian; Weddell, Alexander; Beeby, Stephen Wearable and autonomous computing for future smart cities: open challenges Inproceedings 25th International Conference on Software, Telecommunications and Computer Networks, 2017. @inproceedings{soton414077b, title = {Wearable and autonomous computing for future smart cities: open challenges}, author = {Domenico Balsamo and Geoff V. Merrett and Bahareh Zaghari and Yang Wei and Sarvapali Ramchurn and Sebastian Stein and Alexander Weddell and Stephen Beeby}, url = {https://eprints.soton.ac.uk/414077/}, year = {2017}, date = {2017-09-01}, booktitle = {25th International Conference on Software, Telecommunications and Computer Networks}, abstract = {The promise of smart cities offers the potential to change the way we live, and refers to the integration of IoT systems for people-centred applications, together with the collection and processing of data, and associated decision making. Central to the realization of this are wearable and autonomous computing systems. There are considerable challenges that exist in this space that require research across different areas of electronics and computer science; it is this multidisciplinary consideration that is novel to this paper. We consider these challenges from different perspectives, involving research in devices, infrastructure and software. Specifically, the challenges considered are related to IoT systems and networking, autonomous computing, wearable sensors and electronics, and the coordination of collectives comprising human and software agents.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The promise of smart cities offers the potential to change the way we live, and refers to the integration of IoT systems for people-centred applications, together with the collection and processing of data, and associated decision making. Central to the realization of this are wearable and autonomous computing systems. There are considerable challenges that exist in this space that require research across different areas of electronics and computer science; it is this multidisciplinary consideration that is novel to this paper. We consider these challenges from different perspectives, involving research in devices, infrastructure and software. Specifically, the challenges considered are related to IoT systems and networking, autonomous computing, wearable sensors and electronics, and the coordination of collectives comprising human and software agents. |
Diago, Ndeye Arame; Aknine, Samir; Ramchurn, Sarvapali; Shehory, Onn; Sene, Mbaye Distributed negotiation for collective decision-making Inproceedings Proceedings - 2017 International Conference on Tools with Artificial Intelligence, ICTAI 2017, pp. 913–920, IEEE Computer Society Press, 2017. @inproceedings{soton421970, title = {Distributed negotiation for collective decision-making}, author = {Ndeye Arame Diago and Samir Aknine and Sarvapali Ramchurn and Onn Shehory and Mbaye Sene}, url = {https://eprints.soton.ac.uk/421970/}, year = {2017}, date = {2017-06-01}, booktitle = {Proceedings - 2017 International Conference on Tools with Artificial Intelligence, ICTAI 2017}, volume = {2017-November}, pages = {913--920}, publisher = {IEEE Computer Society Press}, abstract = {ensuremath keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } ensuremath<pensuremath>Collective decision-making is a process in which participants make a collective choice from several alternatives. In this paper, we focus on collective decision contexts in which more than two selfish agents negotiate over multiple issues. We specifically consider a case of joint household energy purchase where the concerned households have to define a collective energy contract. The households involved may each be interested only in a subset of the issues at stake. We devise an effective protocol to regulate the interactions among the (household) agents and reduce their reasoning complexity. The mechanism we introduce is fully decentralized, it facilitates multi-lateral negotiation, and it reduces the complexity of the solution despite the inherent complexity of the problem.ensuremath</pensuremath> |
Mike Shann Alper Alan, Sven Seuken Enrico Costanza ; Ramchurn, Sarvapali Save Money or Feel Cozy? A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences Inproceedings Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, 2017. @inproceedings{seuken:etal:2017, title = {Save Money or Feel Cozy? A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences}, author = {Mike Shann, Alper Alan, Sven Seuken, Enrico Costanza and Sarvapali Ramchurn}, year = {2017}, date = {2017-05-02}, booktitle = {Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Fischer, Joel E; Greenhalgh, Chris; Jiang, Wenchao; Ramchurn, Sarvapali D; Wu, Feng; Rodden, Tom In-the-loop or on-the-loop? Interactional arrangements to support team coordination with a planning agent Journal Article Concurrency and Computation: Practice and Experience, 0 (0), 2017, (e4082 cpe.4082). @article{doi:10.1002/cpe.4082, title = {In-the-loop or on-the-loop? Interactional arrangements to support team coordination with a planning agent}, author = {Joel E Fischer and Chris Greenhalgh and Wenchao Jiang and Sarvapali D Ramchurn and Feng Wu and Tom Rodden}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4082}, doi = {10.1002/cpe.4082}, year = {2017}, date = {2017-03-06}, journal = {Concurrency and Computation: Practice and Experience}, volume = {0}, number = {0}, abstract = {Summary In this paper, we present the study of interactional arrangements that support the collaboration of headquarters (HQ), field responders, and a computational planning agent in a time-critical task setting created by a mixed-reality game. Interactional arrangements define the extent to which control is distributed between the collaborative parties. We provide 2 field trials, one to study an “on-the-loop” arrangement in which HQ monitors and intervenes in agent instructions to field players on demand and the other, to study a version that places HQ more tightly “in-the-loop.” The studies provide an understanding of the sociotechnical collaboration between players and the agent in these interactional arrangements by conducting interaction analysis of video recordings and game log data. The first field trial focuses on the collaboration of field responders with the planning agent. Findings highlight how players negotiate the agent guidance within the social interaction of the collocated teams. The second field trial focuses on the collaboration between the automated planning agent and the HQ. We find that the human coordinator and the agent can successfully work together in most cases, with human coordinators inspecting and “correcting” the agent-proposed plans. Through this field trial-driven development process, we generalise interaction design implications of automated planning agents around the themes of supporting common ground and mixed-initiative planning.}, note = {e4082 cpe.4082}, keywords = {}, pubstate = {published}, tppubtype = {article} } Summary In this paper, we present the study of interactional arrangements that support the collaboration of headquarters (HQ), field responders, and a computational planning agent in a time-critical task setting created by a mixed-reality game. Interactional arrangements define the extent to which control is distributed between the collaborative parties. We provide 2 field trials, one to study an “on-the-loop” arrangement in which HQ monitors and intervenes in agent instructions to field players on demand and the other, to study a version that places HQ more tightly “in-the-loop.” The studies provide an understanding of the sociotechnical collaboration between players and the agent in these interactional arrangements by conducting interaction analysis of video recordings and game log data. The first field trial focuses on the collaboration of field responders with the planning agent. Findings highlight how players negotiate the agent guidance within the social interaction of the collocated teams. The second field trial focuses on the collaboration between the automated planning agent and the HQ. We find that the human coordinator and the agent can successfully work together in most cases, with human coordinators inspecting and “correcting” the agent-proposed plans. Through this field trial-driven development process, we generalise interaction design implications of automated planning agents around the themes of supporting common ground and mixed-initiative planning. |
Ayodeji, Opeyemi Abioye ; Prior, Stephen; Thomas, Trevor; Saddington, Peter; Ramchurn, Sarvapali Multimodal human aerobotic interaction Incollection Issa, Tomayess; Kommers, Piet; Issa, Theodora; 'i, Pedro Isa; Issa, Touma B (Ed.): Smart Technology Applications in Business Environments, pp. 39–62, IGI Global, 2017. @incollection{soton406888b, title = {Multimodal human aerobotic interaction}, author = {Ayodeji, Opeyemi Abioye and Stephen Prior and Trevor Thomas and Peter Saddington and Sarvapali Ramchurn}, editor = {Tomayess Issa and Piet Kommers and Theodora Issa and Pedro Isa{'i}as and Touma B. Issa}, url = {https://eprints.soton.ac.uk/406888/}, year = {2017}, date = {2017-03-01}, booktitle = {Smart Technology Applications in Business Environments}, pages = {39--62}, publisher = {IGI Global}, abstract = {This chapter discusses HCI interfaces used in controlling aerial robotic systems (otherwise known as aerobots). The autonomy control level of aerobot is also discussed. However, due to the limitations of existing models, a novel classification model of autonomy, specifically designed for multirotor aerial robots, called the navigation control autonomy (nCA) model is also developed. Unlike the existing models such as the AFRL and ONR, this model is presented in tiers and has a two-dimensional pyramidal structure. This model is able to identify the control void existing beyond tier-one autonomy components modes and to map the upper and lower limits of control interfaces. Two solutions are suggested for dealing with the existing control void and the limitations of the RC joystick controller ? the multimodal HHI-like interface and the unimodal BCI interface. In addition to these, some human factors based performance measurement is recommended, and the plans for further works presented.}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } This chapter discusses HCI interfaces used in controlling aerial robotic systems (otherwise known as aerobots). The autonomy control level of aerobot is also discussed. However, due to the limitations of existing models, a novel classification model of autonomy, specifically designed for multirotor aerial robots, called the navigation control autonomy (nCA) model is also developed. Unlike the existing models such as the AFRL and ONR, this model is presented in tiers and has a two-dimensional pyramidal structure. This model is able to identify the control void existing beyond tier-one autonomy components modes and to map the upper and lower limits of control interfaces. Two solutions are suggested for dealing with the existing control void and the limitations of the RC joystick controller ? the multimodal HHI-like interface and the unimodal BCI interface. In addition to these, some human factors based performance measurement is recommended, and the plans for further works presented. |
Filippo Bistaffa Alessandro Farinelli, Jesús Cerquides Juan Rodríguez-Aguilar A; Ramchurn, Sarvapali D Algorithms for Graph-Constrained Coalition Formation in the Real World Journal Article ACM Transactions on Intelligent Systems and Technology (TIST), 8 (4), 2017. @article{bistaffaetal2017b, title = {Algorithms for Graph-Constrained Coalition Formation in the Real World}, author = {Filippo Bistaffa, Alessandro Farinelli, Jesús Cerquides, Juan A. Rodríguez-Aguilar, and Sarvapali D. Ramchurn}, url = {http://www.sramchurn.com/wp-content/uploads/2017/03/2017tist.pdf}, doi = {http://dx.doi.org/10.1145/3040967}, year = {2017}, date = {2017-02-11}, journal = {ACM Transactions on Intelligent Systems and Technology (TIST)}, volume = {8}, number = {4}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Filippo Bistaffa Georgios Chalkiadakis Alessandro Farinelli; Ramchurn, Sarvapali A Cooperative Game-Theoretic Approach to the Social Ridesharing Problem Journal Article Artificial Intelligence Journal, pp. (accepted), 2017. @article{bistaffa:etal:2017b, title = {A Cooperative Game-Theoretic Approach to the Social Ridesharing Problem}, author = {Filippo Bistaffa Georgios Chalkiadakis, Alessandro Farinelli; Ramchurn, Sarvapali}, url = {http://www.sciencedirect.com/science/article/pii/S0004370217300243}, year = {2017}, date = {2017-02-11}, journal = {Artificial Intelligence Journal}, pages = {(accepted)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Farinelli, Alessandro; Bicego, Manuele; Bistaffa, Filippo; Ramchurn, Sarvapali D A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation with Quality Guarantees Journal Article Engineering Applications of Artificial Intelligence (EAAI), 57 , pp. 170-185, 2017. @article{farinelli:etal:2017, title = {A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation with Quality Guarantees}, author = {Alessandro Farinelli and Manuele Bicego and Filippo Bistaffa and Sarvapali D. Ramchurn}, url = {http://www.sramchurn.com/wp-content/uploads/2017/02/1-s2.0-S0952197616302536-main.pdf}, doi = {http://dx.doi.org/10.1016/j.engappai.2016.12.018}, year = {2017}, date = {2017-02-01}, journal = {Engineering Applications of Artificial Intelligence (EAAI)}, volume = {57}, pages = {170-185}, abstract = {Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context is the coalition structure generation problem, where a set of agents must be partitioned into the best set of coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques (Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link) and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific characteristic functions we can provide such bounds while maintaining polynomial computational costs and memory requirements. We empirically evaluate the different variants of the C-Link algorithm on two synthetic benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ridesharing application. In these settings C-Link achieves promising results providing high quality solutions and solving problem involving thousands of agents in few minutes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context is the coalition structure generation problem, where a set of agents must be partitioned into the best set of coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques (Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link) and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific characteristic functions we can provide such bounds while maintaining polynomial computational costs and memory requirements. We empirically evaluate the different variants of the C-Link algorithm on two synthetic benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ridesharing application. In these settings C-Link achieves promising results providing high quality solutions and solving problem involving thousands of agents in few minutes. |
Cruz, Francisco ; Espinosa, Antonio ; Moure, Juan C; Cerquides, Jesus ; Rodriguez-Aguilar, Juan A; Svensson, Kim ; Ramchurn, Sarvapali D Coalition structure generation problems: optimization and parallelization of the IDP algorithm in multicore systems Journal Article Concurrency and Computation: Practice and Experience, 29 (5), pp. e3969–n/a, 2017, ISSN: 1532-0634, (e3969 cpe.3969). @article{CPE:CPE3969, title = {Coalition structure generation problems: optimization and parallelization of the IDP algorithm in multicore systems}, author = {Cruz, Francisco and Espinosa, Antonio and Moure, Juan C. and Cerquides, Jesus and Rodriguez-Aguilar, Juan A. and Svensson, Kim and Ramchurn, Sarvapali D.}, url = {http://dx.doi.org/10.1002/cpe.3969}, doi = {10.1002/cpe.3969}, issn = {1532-0634}, year = {2017}, date = {2017-01-01}, journal = {Concurrency and Computation: Practice and Experience}, volume = {29}, number = {5}, pages = {e3969--n/a}, publisher = {John Wiley & Sons, Ltd}, note = {e3969 cpe.3969}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2016 |
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S J; Moreau, Luc; Rodden, T; Jennings, N R A Disaster Response System based on Human-Agent Collectives Journal Article Journal of Artificial Intelligence Research, 57 , pp. 661-708, 2016. @article{eps374070b, title = {A Disaster Response System based on Human-Agent Collectives}, author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S.J. Roberts and Luc Moreau and T. Rodden and N.R. Jennings}, url = {http://www.jair.org/media/5098/live-5098-9699-jair.pdf}, year = {2016}, date = {2016-12-01}, journal = {Journal of Artificial Intelligence Research}, volume = {57}, pages = {661-708}, abstract = {Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system. |
Garcia, Pedro Garcia; Costanza, Enrico; Verame, Jhim; Ramchurn, Sarvapali D The potential of physical motion cues: changing people?s perception of robots? performance Inproceedings UbiComp 2016: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2016. @inproceedings{eps398009, title = {The potential of physical motion cues: changing people?s perception of robots? performance}, author = {Pedro Garcia Garcia and Enrico Costanza and Jhim Verame and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/398009/}, year = {2016}, date = {2016-09-01}, booktitle = {UbiComp 2016: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing}, publisher = {ACM}, abstract = {Autonomous robotic systems can automatically perform actions on behalf of users in the domestic environment to help people in their daily activities. Such systems aim to reduce users' cognitive and physical workload, and improve wellbeing. While the benefits of these systems are clear, recent studies suggest that users may misconstrue their performance of tasks. We see an opportunity in designing interaction techniques that improve how users perceive the performance of such systems. We report two lab studies (N=16 each) designed to investigate whether showing physical motion, which is showing the process of a system through movement (that is intrinsic to the system's task), of an autonomous system as it completes its task, affects how users perceive its performance. To ensure our studies are ecologically valid and to motivate participants to provide thoughtful responses we adopted consensus-oriented financial incentives. Our results suggest that physical presence does yield higher performance ratings.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Autonomous robotic systems can automatically perform actions on behalf of users in the domestic environment to help people in their daily activities. Such systems aim to reduce users' cognitive and physical workload, and improve wellbeing. While the benefits of these systems are clear, recent studies suggest that users may misconstrue their performance of tasks. We see an opportunity in designing interaction techniques that improve how users perceive the performance of such systems. We report two lab studies (N=16 each) designed to investigate whether showing physical motion, which is showing the process of a system through movement (that is intrinsic to the system's task), of an autonomous system as it completes its task, affects how users perceive its performance. To ensure our studies are ecologically valid and to motivate participants to provide thoughtful responses we adopted consensus-oriented financial incentives. Our results suggest that physical presence does yield higher performance ratings. |
Truong, Ngoc Cuong; Baarslag, Tim; Ramchurn, Sarvapali D; Tran-Thanh, Long Interactive scheduling of appliance usage in the home Inproceedings 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 869–875, 2016. @inproceedings{eps396670, title = {Interactive scheduling of appliance usage in the home}, author = {Ngoc Cuong Truong and Tim Baarslag and Sarvapali D. Ramchurn and Long Tran-Thanh}, url = {http://eprints.soton.ac.uk/396670/}, year = {2016}, date = {2016-07-12}, booktitle = {25th International Joint Conference on Artificial Intelligence (IJCAI-16)}, pages = {869--875}, abstract = {We address the problem of recommending an appliance usage schedule to the homeowner which balances between maximising total savings and maintaining sufficient user convenience. An important challenge within this problem is how to elicit the the user preferences with low intrusiveness, in order to identify new schedules with high cost savings, that still lies within the user?s comfort zone. To tackle this problem we propose iDR, an interactive system for generating personalised appliance usage scheduling recommendations that maximise savings and convenience with minimal intrusiveness. In particular, our system learns when to stop interacting with the user during the preference elicitation process, in order to keep the bother cost (e.g., the amount of time the user spends, or the cognitive cost of interacting) minimal. We demonstrate through extensive empirical evaluation on real?world data that our approach improves savings by up to 35%, while maintaining a significantly lower bother cost, compared to state-of the-art benchmarks}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We address the problem of recommending an appliance usage schedule to the homeowner which balances between maximising total savings and maintaining sufficient user convenience. An important challenge within this problem is how to elicit the the user preferences with low intrusiveness, in order to identify new schedules with high cost savings, that still lies within the user?s comfort zone. To tackle this problem we propose iDR, an interactive system for generating personalised appliance usage scheduling recommendations that maximise savings and convenience with minimal intrusiveness. In particular, our system learns when to stop interacting with the user during the preference elicitation process, in order to keep the bother cost (e.g., the amount of time the user spends, or the cognitive cost of interacting) minimal. We demonstrate through extensive empirical evaluation on real?world data that our approach improves savings by up to 35%, while maintaining a significantly lower bother cost, compared to state-of the-art benchmarks |
Baker, Chris; Ramchurn, Sarvapali D; Teacy, Luke; Jennings, Nicholas Planning search and rescue missions for UAV teams Inproceedings PAIS 2016: Conference on Prestigious Applications of Intelligent Systems at ECAI 2016, 2016. @inproceedings{eps396996, title = {Planning search and rescue missions for UAV teams}, author = {Chris Baker and Sarvapali D. Ramchurn and Luke Teacy and Nicholas Jennings}, url = {http://eprints.soton.ac.uk/396996/}, year = {2016}, date = {2016-06-01}, booktitle = {PAIS 2016: Conference on Prestigious Applications of Intelligent Systems at ECAI 2016}, abstract = {The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite re- mote sensing, or manned reconnaissance. In particular, such information can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when planning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solution, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite re- mote sensing, or manned reconnaissance. In particular, such information can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when planning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solution, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data. |
Fisher, Joel; Crabtree, Andy; Rodden, Tom; Colley, James; Costanza, Enrico; Jewell, Michael; Ramchurn, Sarvapali "Just whack it on until it gets hot, then turn it off": Working with IoT Data in the Home Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. @inproceedings{eps385056, title = {"Just whack it on until it gets hot, then turn it off": Working with IoT Data in the Home}, author = {Joel Fisher and Andy Crabtree and Tom Rodden and James Colley and Enrico Costanza and Michael Jewell and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/385056/}, year = {2016}, date = {2016-05-01}, booktitle = {The SIGCHI Conference on Human Factors in Computing Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Zhao, Dengji; Ramchurn, Sarvapali D; Jennings, Nicholas Fault tolerant mechanism design for general task allocation Inproceedings The 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), International Foundation for Autonomous Agents and Multiagent Systems, 2016. @inproceedings{eps388365, title = {Fault tolerant mechanism design for general task allocation}, author = {Dengji Zhao and Sarvapali D. Ramchurn and Nicholas Jennings}, url = {http://eprints.soton.ac.uk/388365/}, year = {2016}, date = {2016-05-01}, booktitle = {The 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016)}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, abstract = {We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account (i.e., fault tolerant). To achieve this, we show that the post-execution verification (PEV)-based mechanism presented by Porter et al. (2008) is applicable if and only if agents' valuations are risk-neutral (i.e., the solution is almost universal). We then consider a more advanced setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still applicable if and only if the trust aggregation is multilinear. Given this characterisation, we further demonstrate how this mechanism can be successfully applied in a real-world setting. Finally, we draw the parallels between our results and the literature of efficient mechanism design with general interdependent valuations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account (i.e., fault tolerant). To achieve this, we show that the post-execution verification (PEV)-based mechanism presented by Porter et al. (2008) is applicable if and only if agents' valuations are risk-neutral (i.e., the solution is almost universal). We then consider a more advanced setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still applicable if and only if the trust aggregation is multilinear. Given this characterisation, we further demonstrate how this mechanism can be successfully applied in a real-world setting. Finally, we draw the parallels between our results and the literature of efficient mechanism design with general interdependent valuations. |
Wu, Feng; Ramchurn, Sarvapali D; Chen, Xiaoping Coordinating human-UAV teams in disaster response Inproceedings International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 524–530, 2016. @inproceedings{eps393725, title = {Coordinating human-UAV teams in disaster response}, author = {Feng Wu and Sarvapali D. Ramchurn and Xiaoping Chen}, url = {http://eprints.soton.ac.uk/393725/}, year = {2016}, date = {2016-04-01}, booktitle = {International Joint Conference on Artificial Intelligence (IJCAI-16)}, pages = {524--530}, abstract = {We consider a disaster response scenario where emergency responders have to complete rescue tasks in dynamic and uncertain environment with the assistance of multiple UAVs to collect information about the disaster space. To capture the uncertainty and partial observability of the domain, we model this problem as a POMDP. However, the resulting model is computationally intractable and cannot be solved by most existing POMDP solvers due to the large state and action spaces. By exploiting the problem structure we propose a novel online planning algorithm to solve this model. Specifically, we generate plans for the responders based on Monte-Carlo simulations and compute actions for the UAVs according to the value of information. Our empirical results confirm that our algorithm significantly outperforms the state-of-the-art both in time and solution quality.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider a disaster response scenario where emergency responders have to complete rescue tasks in dynamic and uncertain environment with the assistance of multiple UAVs to collect information about the disaster space. To capture the uncertainty and partial observability of the domain, we model this problem as a POMDP. However, the resulting model is computationally intractable and cannot be solved by most existing POMDP solvers due to the large state and action spaces. By exploiting the problem structure we propose a novel online planning algorithm to solve this model. Specifically, we generate plans for the responders based on Monte-Carlo simulations and compute actions for the UAVs according to the value of information. Our empirical results confirm that our algorithm significantly outperforms the state-of-the-art both in time and solution quality. |
Baker, Chris; Ramchurn, Gopal; Teacy, Luke; Jennings, Nicholas Factored Monte-Carlo tree search for coordinating UAVs in disaster response Inproceedings Distributed and Multi-Agent Planning, ICAPS, 2016. @inproceedings{eps393649, title = {Factored Monte-Carlo tree search for coordinating UAVs in disaster response}, author = {Chris Baker and Gopal Ramchurn and Luke Teacy and Nicholas Jennings}, url = {http://eprints.soton.ac.uk/393649/}, year = {2016}, date = {2016-04-01}, booktitle = {Distributed and Multi-Agent Planning}, publisher = {ICAPS}, abstract = {The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. However, an increase in the availability of real-time data about a disaster from sources such as crowd reports or satellites presents a valuable source of information to drive the planning of UAV flight paths over a space in order to discover people who are in danger. Nevertheless challenges remain when planning over the very large action spaces that result. To this end, we introduce the survivor discovery problem and present as our solution, the first example of a factored coordinated Monte Carlo tree search algorithm to perform decentralised path planning for multiple coordinated UAVs. Our evaluation against standard benchmarks show that our algorithm, Co-MCTS, is able to find more casualties faster than standard approaches by 10% or more on simulations with real-world data from the 2010 Haiti earthquake.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. However, an increase in the availability of real-time data about a disaster from sources such as crowd reports or satellites presents a valuable source of information to drive the planning of UAV flight paths over a space in order to discover people who are in danger. Nevertheless challenges remain when planning over the very large action spaces that result. To this end, we introduce the survivor discovery problem and present as our solution, the first example of a factored coordinated Monte Carlo tree search algorithm to perform decentralised path planning for multiple coordinated UAVs. Our evaluation against standard benchmarks show that our algorithm, Co-MCTS, is able to find more casualties faster than standard approaches by 10% or more on simulations with real-world data from the 2010 Haiti earthquake. |
Verame, Jhim Kiel M; Costanza, Enrico; Ramchurn, Sarvapali The SIGCHI Conference on Human Factors in Computing Systems, 2016. @inproceedings{eps385069b, title = {The Effect of Displaying System Confidence Information on the Usage of Autonomous Systems for Non-specialist Applications: A Lab Study}, author = {Jhim Kiel M. Verame and Enrico Costanza and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/385069/}, year = {2016}, date = {2016-01-01}, booktitle = {The SIGCHI Conference on Human Factors in Computing Systems}, abstract = {Autonomous systems are designed to take actions on behalf of users, acting autonomously upon data from sensors or online sources. As such, the design of interaction mechanisms that enable users to understand the operation of autonomous systems and flexibly delegate or regain control is an open challenge for HCI. Against this background, in this paper we report on a lab study designed to investigate whether displaying the confidence of an autonomous system about the quality of its work, which we call its confidence information, can improve user acceptance and interaction with autonomous systems. The results demonstrate that confidence information encourages the usage of the autonomous system we tested, compared to a situation where such information is not available. Furthermore, an additional contribution of our work is the methodology we employ to study users' incentives to do work in collaboration with the autonomous system. In experiments comparing different incentive strategies, our results indicate that our translation of behavioural economics research methods to HCI can support the study of interactions with autonomous systems in the lab.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Autonomous systems are designed to take actions on behalf of users, acting autonomously upon data from sensors or online sources. As such, the design of interaction mechanisms that enable users to understand the operation of autonomous systems and flexibly delegate or regain control is an open challenge for HCI. Against this background, in this paper we report on a lab study designed to investigate whether displaying the confidence of an autonomous system about the quality of its work, which we call its confidence information, can improve user acceptance and interaction with autonomous systems. The results demonstrate that confidence information encourages the usage of the autonomous system we tested, compared to a situation where such information is not available. Furthermore, an additional contribution of our work is the methodology we employ to study users' incentives to do work in collaboration with the autonomous system. In experiments comparing different incentive strategies, our results indicate that our translation of behavioural economics research methods to HCI can support the study of interactions with autonomous systems in the lab. |
Alan, Alper Turan; Shann, Mike; Costanza, Enrico; Ramchurn, Sarvapali; Seuken, Sven It is too hot: an in-situ study of three designs for heating Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. @inproceedings{eps385045b, title = {It is too hot: an in-situ study of three designs for heating}, author = {Alper Turan Alan and Mike Shann and Enrico Costanza and Sarvapali Ramchurn and Sven Seuken}, url = {http://eprints.soton.ac.uk/385045/}, year = {2016}, date = {2016-01-01}, booktitle = {The SIGCHI Conference on Human Factors in Computing Systems}, abstract = {Smart technologies are becoming increasingly ubiquitous, and consequently transforming our lives. Domestic energy use is one of the most talked domain that people may greatly benefit from these technologies. Given this, it is important to understand interactions with smart systems within people?s everyday lives. To this end, we developed and deployed the first heating system that allows its users to control their home heating with real-time prices. In particular, we implemented three different designs of our heating system, and evaluated them with 30 UK households in a four-week in the wild study. Our findings through thematic analysis show that our participants formed different understandings and expectations of the system, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. These findings contribute to our understanding of interactions with smart energy systems and provide key design implications for developing them.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Smart technologies are becoming increasingly ubiquitous, and consequently transforming our lives. Domestic energy use is one of the most talked domain that people may greatly benefit from these technologies. Given this, it is important to understand interactions with smart systems within people?s everyday lives. To this end, we developed and deployed the first heating system that allows its users to control their home heating with real-time prices. In particular, we implemented three different designs of our heating system, and evaluated them with 30 UK households in a four-week in the wild study. Our findings through thematic analysis show that our participants formed different understandings and expectations of the system, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. These findings contribute to our understanding of interactions with smart energy systems and provide key design implications for developing them. |
Bandhyopadhyay, Sambaran; Narayanam, Ramasuri; Kumar, Pratyush; Ramchurn, Sarvapali Dyanand; Arya, Vijay An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid Inproceedings Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI), AAAI Press, 2016. @inproceedings{eps386417, title = {An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid}, author = {Sambaran Bandhyopadhyay and Ramasuri Narayanam and Pratyush Kumar and Sarvapali Dyanand Ramchurn and Vijay Arya}, url = {http://eprints.soton.ac.uk/386417/}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI)}, publisher = {AAAI Press}, abstract = {In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes.We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously.We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes.We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously.We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities. |
Calliere, Romain ; Aknine, Samir ; Nongaillard, Antoine ; Ramchurn, Sarvapali Managing energy markets in future smart grids using bilateral contracts Inproceedings European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016. @inproceedings{cailliere:hal-01329606, title = {Managing energy markets in future smart grids using bilateral contracts}, author = {Calliere, Romain and Aknine, Samir and Nongaillard, Antoine and Ramchurn, Sarvapali}, url = {https://hal.archives-ouvertes.fr/hal-01329606}, year = {2016}, date = {2016-01-01}, booktitle = {European Conference on Artificial Intelligence (ECAI)}, address = {The Hague, Netherlands}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article IEEE Trans. Cybernetics, 46 (12), pp. 3364–3376, 2016. @article{DBLP:journals/tcyb/ChenWSCR16, title = {Decentralized Patrolling Under Constraints in Dynamic Environments}, author = {Shaofei Chen and Feng Wu and Lincheng Shen and Jing Chen and Sarvapali D Ramchurn}, url = {https://doi.org/10.1109/TCYB.2015.2505737}, doi = {10.1109/TCYB.2015.2505737}, year = {2016}, date = {2016-01-01}, journal = {IEEE Trans. Cybernetics}, volume = {46}, number = {12}, pages = {3364--3376}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
-, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Inproceedings 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016, pp. 302–307, 2016. @inproceedings{DBLP:conf/smartgridcomm/KoufakisRBR16, title = {Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer}, author = {Alexandros - and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn}, url = {https://doi.org/10.1109/SmartGridComm.2016.7778778}, doi = {10.1109/SmartGridComm.2016.7778778}, year = {2016}, date = {2016-01-01}, booktitle = {2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016}, pages = {302--307}, crossref = {DBLP:conf/smartgridcomm/2016}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2015 |
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali CrowdAR: augmenting live video with a real-time crowd Inproceedings HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing, 2015. @inproceedings{eps382948, title = {CrowdAR: augmenting live video with a real-time crowd}, author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/382948/}, year = {2015}, date = {2015-11-01}, booktitle = {HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing}, abstract = {Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd. |
Filippo Bistaffa Georgios Chalkiadakis, Alessandro Farinelli ; Ramchurn, Sarvapali D Recommending Fair Payments for Large-Scale Social Ridesharing Inproceedings ACM Conference on Recommender Systems (Recsys), 2015. @inproceedings{bistaffaetal2015, title = {Recommending Fair Payments for Large-Scale Social Ridesharing}, author = {Filippo Bistaffa, Georgios Chalkiadakis, Alessandro Farinelli, and Sarvapali D. Ramchurn}, url = {http://www.sramchurn.com/wp-content/uploads/2017/02/2015recsys.pdf}, year = {2015}, date = {2015-09-16}, booktitle = {ACM Conference on Recommender Systems (Recsys)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alan, Alper T; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N R Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015. @inproceedings{eps378696, title = {Managing energy tariffs with agents: a field study of a future smart energy system at home}, author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings}, url = {http://eprints.soton.ac.uk/378696/}, year = {2015}, date = {2015-07-01}, booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alan, Alper T; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N R Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp), 2015. @inproceedings{eps378696b, title = {Managing energy tariffs with agents: a field study of a future smart energy system at home}, author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings}, url = {http://eprints.soton.ac.uk/378696/}, year = {2015}, date = {2015-07-01}, booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Joel E. Fischer Stuart Reeves, Tom Rodden Steven Reece Sarvapali Ramchurn D; Jones, David Building a Bird's Eye View: Collaborative Work Inproceedings Proceedings of SIGCHI (To appear), 2015. @inproceedings{fischer:etal:2015, title = {Building a Bird\'s Eye View: Collaborative Work }, author = {Joel E. Fischer, Stuart Reeves, Tom Rodden, Steven Reece, Sarvapali D. Ramchurn, and David Jones}, url = {http://www.sramchurn.com/wp-content/uploads/2015/01/pn1018-fischerA.pdf}, year = {2015}, date = {2015-05-01}, booktitle = {Proceedings of SIGCHI (To appear)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Long Tran-Thanh Trung Dong Huynh, Avi Rosenfeld Sarvapali Ramchurn Nicholas Jennings D R Crowdsourcing Complex Workflows under Budget Constraints Inproceedings Proceedings of the AAAI Conference, AAAI, 2015. @inproceedings{tranh:Etal:2015, title = {Crowdsourcing Complex Workflows under Budget Constraints}, author = {Long Tran-Thanh, Trung Dong Huynh, Avi Rosenfeld, Sarvapali D. Ramchurn, Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/372107/}, year = {2015}, date = {2015-01-25}, booktitle = {Proceedings of the AAAI Conference}, publisher = {AAAI}, abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper. |
Filippo Bistaffa Alessandro Farinelli, Sarvapali Ramchurn D Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Inproceedings Proceedings of the AAAI Conference, 2015. @inproceedings{bistaffa:etal:2015, title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing}, author = {Filippo Bistaffa, Alessandro Farinelli, Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/372048/}, year = {2015}, date = {2015-01-25}, booktitle = {Proceedings of the AAAI Conference}, abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds). |
Dengji Zhao Sarvapali D. Ramchurn, Enrico Gerding H; Jennings, Nicholas R Balanced Trade Reduction for Dual-Role Exchange Markets Inproceedings Proceedings of the AAAI Conference, 2015. @inproceedings{zhao:etal:2015, title = {Balanced Trade Reduction for Dual-Role Exchange Markets}, author = {Dengji Zhao, Sarvapali D. Ramchurn, Enrico H. Gerding, and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/372050/}, year = {2015}, date = {2015-01-25}, booktitle = {Proceedings of the AAAI Conference}, abstract = {We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit. |
Emmanouil Rigas Sarvapali D. Ramchurn, Nick Bassiliades Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article IEEE Transactions on Intelligent Transportation Systems, 2015. @article{rigas:etal:2015, title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey}, author = {Emmanouil Rigas, Sarvapali D. Ramchurn, Nick Bassiliades}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29}, year = {2015}, date = {2015-01-16}, journal = {IEEE Transactions on Intelligent Transportation Systems}, abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space. |
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali Real-time opinion aggregation methods for crowd robotics Inproceedings Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015. @inproceedings{eps375287, title = {Real-time opinion aggregation methods for crowd robotics}, author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/375287/}, year = {2015}, date = {2015-01-01}, booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)}, abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios |
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S J; Moreau, Luc; Rodden, T; Jennings, N R HAC-ER: A disaster response system based on human-agent collectives Inproceedings 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015. @inproceedings{eps374070, title = {HAC-ER: A disaster response system based on human-agent collectives}, author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S.J. Roberts and Luc Moreau and T. Rodden and N.R. Jennings}, url = {http://eprints.soton.ac.uk/374070/}, year = {2015}, date = {2015-01-01}, booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems}, abstract = { This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations. |
Ramchurn, Sarvapali; Wu, Feng; Fischer, Joel; Reece, Steven; Jiang, Wenchao; Roberts, Stephen J; Rodden, Tom; Jennings, Nicholas R Human-agent collaboration for disaster response Journal Article Journal of Autonomous Agents and Multi-Agent Systems, pp. 1–30, 2015. @article{eps374063, title = {Human-agent collaboration for disaster response}, author = {Sarvapali Ramchurn and Feng Wu and Joel Fischer and Steven Reece and Wenchao Jiang and Stephen J. Roberts and Tom Rodden and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/374063/}, year = {2015}, date = {2015-01-01}, journal = {Journal of Autonomous Agents and Multi-Agent Systems}, pages = {1--30}, publisher = {Springer}, abstract = {In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked. |
Alam, Muddasser; Gerding, Enrico H; Rogers, Alex; Ramchurn, Sarvapali D A scalable, decentralised multi-issue negotiation protocol for energy exchange Inproceedings International Joint Conference on Artificial Intelligence (IJCAI), 2015. @inproceedings{eps376618, title = {A scalable, decentralised multi-issue negotiation protocol for energy exchange}, author = {Muddasser Alam and Enrico H. Gerding and Alex Rogers and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/376618/}, year = {2015}, date = {2015-01-01}, booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)}, abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes. |
Wu, Feng; Ramchurn, Sarvapali; Jiang, Wenchao; Fischer, Joel; Rodden, Tom; Jennings, Nicholas R Agile Planning for Real-World Disaster Response Inproceedings International Joint Conference on Artificial Intelligence, 2015. @inproceedings{eps377186, title = {Agile Planning for Real-World Disaster Response}, author = {Feng Wu and Sarvapali Ramchurn and Wenchao Jiang and Joel Fischer and Tom Rodden and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/377186/}, year = {2015}, date = {2015-01-01}, booktitle = {International Joint Conference on Artificial Intelligence}, abstract = {We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans. |
Publications
2018 |
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article Artificial Intelligence, 262 , pp. 248–278, 2018. |
The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot Inproceedings 19th Towards Autonomous Robotic Systems (TAROS) Conference 2018, pp. 423–437, Springer, 2018. |
Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks Inproceedings 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18), 2018. |
IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018, pp. 842–845, IEEE, 2018. |
Seeing (movement) is believing: the effect of motion on perception of automatic systems performance Journal Article Human-Computer Interaction, pp. 1-51, 2018. |
On the distinctiveness of the electricity load profile Journal Article Pattern Recognition, 74 (Supplement C), pp. 317–325, 2018, ISSN: 0031-3203. |
Algorithms for fair load shedding in developing countries Inproceedings Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1590-1596, 2018. |
Algorithms to manage load shedding events in developing countries Inproceedings Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS), pp. 2034-2036, 2018. |
Learning from the veg box: Designing unpredictability in agency delegation Inproceedings CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018. |
A Decentralised Approach to Intersection Traffic Management Inproceedings Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden., pp. 527–533, 2018. |
Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs Journal Article Comput. J., 61 (11), pp. 1639–1666, 2018. |
A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent Systems Inproceedings Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, pp. 1604–1612, 2018. |
EVLibSim: A tool for the simulation of electric vehicles' charging stations using the EVLib library Journal Article Simulation Modelling Practice and Theory, 87 , pp. 99–119, 2018. |
2017 |
Wearable and autonomous computing for future smart cities: open challenges Inproceedings 25th International Conference on Software, Telecommunications and Computer Networks, 2017. |
Distributed negotiation for collective decision-making Inproceedings Proceedings - 2017 International Conference on Tools with Artificial Intelligence, ICTAI 2017, pp. 913–920, IEEE Computer Society Press, 2017. |
Save Money or Feel Cozy? A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences Inproceedings Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, 2017. |
In-the-loop or on-the-loop? Interactional arrangements to support team coordination with a planning agent Journal Article Concurrency and Computation: Practice and Experience, 0 (0), 2017, (e4082 cpe.4082). |
Multimodal human aerobotic interaction Incollection Issa, Tomayess; Kommers, Piet; Issa, Theodora; 'i, Pedro Isa; Issa, Touma B (Ed.): Smart Technology Applications in Business Environments, pp. 39–62, IGI Global, 2017. |
Algorithms for Graph-Constrained Coalition Formation in the Real World Journal Article ACM Transactions on Intelligent Systems and Technology (TIST), 8 (4), 2017. |
A Cooperative Game-Theoretic Approach to the Social Ridesharing Problem Journal Article Artificial Intelligence Journal, pp. (accepted), 2017. |
A Hierarchical Clustering Approach to Large-scale Near-optimal Coalition Formation with Quality Guarantees Journal Article Engineering Applications of Artificial Intelligence (EAAI), 57 , pp. 170-185, 2017. |
Coalition structure generation problems: optimization and parallelization of the IDP algorithm in multicore systems Journal Article Concurrency and Computation: Practice and Experience, 29 (5), pp. e3969–n/a, 2017, ISSN: 1532-0634, (e3969 cpe.3969). |
2016 |
A Disaster Response System based on Human-Agent Collectives Journal Article Journal of Artificial Intelligence Research, 57 , pp. 661-708, 2016. |
The potential of physical motion cues: changing people?s perception of robots? performance Inproceedings UbiComp 2016: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2016. |
Interactive scheduling of appliance usage in the home Inproceedings 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 869–875, 2016. |
Planning search and rescue missions for UAV teams Inproceedings PAIS 2016: Conference on Prestigious Applications of Intelligent Systems at ECAI 2016, 2016. |
"Just whack it on until it gets hot, then turn it off": Working with IoT Data in the Home Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. |
Fault tolerant mechanism design for general task allocation Inproceedings The 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), International Foundation for Autonomous Agents and Multiagent Systems, 2016. |
Coordinating human-UAV teams in disaster response Inproceedings International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 524–530, 2016. |
Factored Monte-Carlo tree search for coordinating UAVs in disaster response Inproceedings Distributed and Multi-Agent Planning, ICAPS, 2016. |
The SIGCHI Conference on Human Factors in Computing Systems, 2016. |
It is too hot: an in-situ study of three designs for heating Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. |
An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid Inproceedings Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI), AAAI Press, 2016. |
Managing energy markets in future smart grids using bilateral contracts Inproceedings European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016. |
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article IEEE Trans. Cybernetics, 46 (12), pp. 3364–3376, 2016. |
Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Inproceedings 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016, pp. 302–307, 2016. |
2015 |
CrowdAR: augmenting live video with a real-time crowd Inproceedings HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing, 2015. |
Recommending Fair Payments for Large-Scale Social Ridesharing Inproceedings ACM Conference on Recommender Systems (Recsys), 2015. |
Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015. |
Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp), 2015. |
Building a Bird's Eye View: Collaborative Work Inproceedings Proceedings of SIGCHI (To appear), 2015. |
Crowdsourcing Complex Workflows under Budget Constraints Inproceedings Proceedings of the AAAI Conference, AAAI, 2015. |
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Inproceedings Proceedings of the AAAI Conference, 2015. |
Balanced Trade Reduction for Dual-Role Exchange Markets Inproceedings Proceedings of the AAAI Conference, 2015. |
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article IEEE Transactions on Intelligent Transportation Systems, 2015. |
Real-time opinion aggregation methods for crowd robotics Inproceedings Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015. |
HAC-ER: A disaster response system based on human-agent collectives Inproceedings 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015. |
Human-agent collaboration for disaster response Journal Article Journal of Autonomous Agents and Multi-Agent Systems, pp. 1–30, 2015. |
A scalable, decentralised multi-issue negotiation protocol for energy exchange Inproceedings International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
Agile Planning for Real-World Disaster Response Inproceedings International Joint Conference on Artificial Intelligence, 2015. |
