2017 |
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} } |
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} } |
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. |
Ramchurn, Sarvapali; Fischer, Joel; Ikuno, Yuki; Wu, Feng; Flann, Jack; Waldock, Antony A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments Inproceedings International Joint Conference on Artificial Intelligence, 2015. @inproceedings{eps377185, title = {A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments}, author = {Sarvapali Ramchurn and Joel Fischer and Yuki Ikuno and Feng Wu and Jack Flann and Antony Waldock}, url = {http://eprints.soton.ac.uk/377185/}, year = {2015}, date = {2015-01-01}, booktitle = {International Joint Conference on Artificial Intelligence}, abstract = {We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy. |
Holyhead, James C; Ramchurn, Sarvapali D; Rogers, Alex Consumer Targeting in Residential Demand Response Programmes Inproceedings Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3. @inproceedings{Holyhead:2015:CTR:2768510.2768531, title = {Consumer Targeting in Residential Demand Response Programmes}, author = {Holyhead, James C. and Ramchurn, Sarvapali D. and Rogers, Alex}, url = {http://doi.acm.org/10.1145/2768510.2768531}, isbn = {978-1-4503-3609-3}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems}, pages = {7--16}, publisher = {ACM}, address = {Bangalore, India}, series = {e-Energy '15}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Chen, Shaofei ; Wu, Feng ; Shen, Lincheng ; Chen, Jing ; Ramchurn, Sarvapali D Multi-Agent Patrolling under Uncertainty and Threats Journal Article PLoS ONE, 10 (6), pp. e0130154, 2015, ISBN: 1932-6203. @article{chen:etal:2016, title = {Multi-Agent Patrolling under Uncertainty and Threats}, author = {Chen, Shaofei and Wu, Feng and Shen, Lincheng and Chen, Jing and Ramchurn, Sarvapali D}, editor = {Deng, Yong}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472811/}, doi = {10.1371/journal.pone.0130154}, isbn = {1932-6203}, year = {2015}, date = {2015-01-01}, journal = {PLoS ONE}, volume = {10}, number = {6}, pages = {e0130154}, publisher = {Public Library of Science}, abstract = {We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains. |
Chen, S; Wu, F; Shen, L; Chen, J; Ramchurn, S D Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article Cybernetics, IEEE Transactions on, PP (99), pp. 1-13, 2015, ISSN: 2168-2267. @article{7362160, title = {Decentralized Patrolling Under Constraints in Dynamic Environments}, author = {Chen, S. and Wu, F. and Shen, L. and Chen, J. and Ramchurn, S.D.}, doi = {10.1109/TCYB.2015.2505737}, issn = {2168-2267}, year = {2015}, date = {2015-01-01}, journal = {Cybernetics, IEEE Transactions on}, volume = {PP}, number = {99}, pages = {1-13}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2014 |
M. Bicego F. Recchia, Farinelli Ramchurn Grosso A S D E Behavioural biometrics using electricity load profiles Journal Article Proceedings of the International Conference on Pattern Recognition, 2014. @article{bicego:etal:2014, title = {Behavioural biometrics using electricity load profiles}, author = {M. Bicego, F. Recchia, A. Farinelli, S. D. Ramchurn, E. Grosso}, url = {http://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf}, year = {2014}, date = {2014-08-24}, journal = {Proceedings of the International Conference on Pattern Recognition}, abstract = {Modelling behavioural biometric patterns is a key issue for modern user centric applications, aimed at better monitoring users’ activities, understanding their habits and detecting their identity. Following this trend, this paper investigates whether the electrical energy consumption of a user can be a distinctive behavioural biometric trait. In particular we analyse daily and weekly load profiles showing that they are closely related to the identity of the users. Hence, we believe that this level of analysis can open interesting application scenarios in the field of energy management and it provides a good working framework for the continuous development of smart environments with demonstrable benefits on real-world implementations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Modelling behavioural biometric patterns is a key issue for modern user centric applications, aimed at better monitoring users’ activities, understanding their habits and detecting their identity. Following this trend, this paper investigates whether the electrical energy consumption of a user can be a distinctive behavioural biometric trait. In particular we analyse daily and weekly load profiles showing that they are closely related to the identity of the users. Hence, we believe that this level of analysis can open interesting application scenarios in the field of energy management and it provides a good working framework for the continuous development of smart environments with demonstrable benefits on real-world implementations. |
Alan, Alper; Costanza, Enrico; Fischer, J; Ramchurn, Sarvapali; Rodden, T; Jennings, N R A field study of human-agent interaction for electricity tariff switching Inproceedings Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014. @inproceedings{eps360820, title = {A field study of human-agent interaction for electricity tariff switching}, author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N.R. Jennings}, url = {http://eprints.soton.ac.uk/360820/}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems}, abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom. |
Costanza, Enrico; Fischer, Joel E; Colley, James A; Rodden, Tom; Ramchurn, Sarvapali; Jennings, Nicholas R Doing the laundry with agents: a field trial of a future smart energy system in the home Inproceedings ACM CHI Conference on Human Factors in Computing Systems 2014, pp. 813–822, ACM 2014. @inproceedings{eps361173, title = {Doing the laundry with agents: a field trial of a future smart energy system in the home}, author = {Enrico Costanza and Joel E Fischer and James A Colley and Tom Rodden and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/361173/}, year = {2014}, date = {2014-01-01}, booktitle = {ACM CHI Conference on Human Factors in Computing Systems 2014}, pages = {813--822}, organization = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Tran-Thanh, Long; Huynh, Trung Dong; Rosenfield, A; Ramchurn, Sarvapali; Jennings, Nicholas R BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees Inproceedings 13th International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2014. @inproceedings{eps362321, title = {BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees}, author = {Long Tran-Thanh and Trung Dong Huynh and A Rosenfield and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/362321/}, year = {2014}, date = {2014-01-01}, booktitle = {13th International Conference on Autonomous Agents and Multi-Agent Systems}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Vinyals, Meritxell; Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nicholas R A message-passing approach to decentralised parallel machine scheduling Journal Article The Computer Journal, 2014. @article{eps360818, title = {A message-passing approach to decentralised parallel machine scheduling}, author = {Meritxell Vinyals and Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/360818/}, year = {2014}, date = {2014-01-01}, journal = {The Computer Journal}, publisher = {Oxford University Press}, abstract = {This paper tackles the problem of parallelizing heterogeneous computational tasks across a number of computational nodes (aka agents) where each agent may not be able to perform all the tasks and may have different computational speeds. An equivalent problem can be found in operations research, and it is known as scheduling tasks on unrelated parallel machines (also known as R?Cmax). Given this equivalence observation, we present the spanning tree decentralized task distribution algorithm (ST-DTDA), the first decentralized solution to R?Cmax. ST-DTDA achieves decomposition by means of the min?max algorithm, a member of the generalized distributive law family, that performs inference by message-passing along the edges of a graphical model (known as a junction tree). Specifically, ST-DTDA uses min?max to optimally solve an approximation of the original R?Cmax problem that results from eliminating possible agent-task allocations until it is mapped into an acyclic structure. To eliminate those allocations that are least likely to have an impact on the solution quality, ST-DTDA uses a heuristic approach. Moreover, ST-DTDA provides a per-instance approximation ratio that guarantees that the makespan of its solution (optimal in the approximated R?Cmax problem) is not more than a factor ensuremathrho times the makespan of the optimal of the original problem. In our empirical evaluation of ST-DTDA, we show that ST-DTDA, with a min-regret heuristic, converges to solutions that are between 78 and 95% optimal whilst providing approximation ratios lower than 3.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper tackles the problem of parallelizing heterogeneous computational tasks across a number of computational nodes (aka agents) where each agent may not be able to perform all the tasks and may have different computational speeds. An equivalent problem can be found in operations research, and it is known as scheduling tasks on unrelated parallel machines (also known as R?Cmax). Given this equivalence observation, we present the spanning tree decentralized task distribution algorithm (ST-DTDA), the first decentralized solution to R?Cmax. ST-DTDA achieves decomposition by means of the min?max algorithm, a member of the generalized distributive law family, that performs inference by message-passing along the edges of a graphical model (known as a junction tree). Specifically, ST-DTDA uses min?max to optimally solve an approximation of the original R?Cmax problem that results from eliminating possible agent-task allocations until it is mapped into an acyclic structure. To eliminate those allocations that are least likely to have an impact on the solution quality, ST-DTDA uses a heuristic approach. Moreover, ST-DTDA provides a per-instance approximation ratio that guarantees that the makespan of its solution (optimal in the approximated R?Cmax problem) is not more than a factor ensuremathrho times the makespan of the optimal of the original problem. In our empirical evaluation of ST-DTDA, we show that ST-DTDA, with a min-regret heuristic, converges to solutions that are between 78 and 95% optimal whilst providing approximation ratios lower than 3. |
Fischer, J E; Jiang, W; Kerne, A; Greenhalgh, C; Ramchurn, Sarvapali D; Reece, Steven; Pantidi, N; Rodden, T Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game. Inproceedings 11th Int. Conference on the Design of Cooperative Systems (COOP ?14), 2014. @inproceedings{orchid192, title = {Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game.}, author = {J.E. Fischer and W Jiang and A Kerne and C Greenhalgh and Sarvapali D Ramchurn and Steven Reece and N Pantidi and T Rodden}, year = {2014}, date = {2014-01-01}, booktitle = {11th Int. Conference on the Design of Cooperative Systems (COOP ?14)}, howpublished = {http://www.orchid.ac.uk/eprints/192/1/COOP2014-Fischer-author-version.pdf}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Jiang, W; Fischer, J E; Greenhalgh, C; Ramchurn, Sarvapali D; Wu, Feng; Jennings, Nicholas R; Rodden, T Social Implications of Agent-based Planning Support for Human Teams. Inproceedings 2014 Int. Conference on Collaboration Technologies and Systems, 2014. @inproceedings{orchid191, title = {Social Implications of Agent-based Planning Support for Human Teams.}, author = {W Jiang and J.E. Fischer and C Greenhalgh and Sarvapali D Ramchurn and Feng Wu and Nicholas R Jennings and T Rodden}, year = {2014}, date = {2014-01-01}, booktitle = {2014 Int. Conference on Collaboration Technologies and Systems}, howpublished = {http://www.orchid.ac.uk/eprints/191/1/CTS2014-Jiang-author-version.pdf}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Pawlowski, Krzysztof; Kurach, Karol; Svensson, Kim; Ramchurn, Sarvapali D; Michalak, Tomasz; Rahwan, Talal Coalition Structure Generation with the Graphics Processing Unit Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, 2014. @inproceedings{orchid176, title = {Coalition Structure Generation with the Graphics Processing Unit}, author = {Krzysztof Pawlowski and Karol Kurach and Kim Svensson and Sarvapali D Ramchurn and Tomasz Michalak and Talal Rahwan}, year = {2014}, date = {2014-01-01}, booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Huynh, Trung Dong; Ebden, Mark; Ramchurn, Sarvapali; Roberts, Stephen; Moreau, Luc Data quality assessment from provenance graphs Inproceedings Provenance Analytics 2014, 2014. @inproceedings{eps365510, title = {Data quality assessment from provenance graphs}, author = {Trung Dong Huynh and Mark Ebden and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau}, url = {http://eprints.soton.ac.uk/365510/}, year = {2014}, date = {2014-01-01}, booktitle = {Provenance Analytics 2014}, abstract = {Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein. |
Jennings, Nicholas R; Moreau, Luc; Nicholson, D; Ramchurn, Sarvapali D; Roberts, S; Rodden, T; Rogers, Alex On human-agent collectives Journal Article Communications of the ACM, 57 (12), pp. 33-42, 2014. @article{eps364593, title = {On human-agent collectives}, author = {Nicholas R. Jennings and Luc Moreau and D Nicholson and Sarvapali D. Ramchurn and S Roberts and T Rodden and Alex Rogers}, url = {http://eprints.soton.ac.uk/364593/}, year = {2014}, date = {2014-01-01}, journal = {Communications of the ACM}, volume = {57}, number = {12}, pages = {33-42}, abstract = {We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented. |
Bistaffa, Filippo; Farinelli, Alessandro; Cerquides, Jesus; Rodriguez-Aguilar, Juan Antonio; Ramchurn, Sarvapali D Anytime Coalition Structure Generation on Synergy Graphs Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, pp. 13-20, 2014. @inproceedings{orchid175, title = {Anytime Coalition Structure Generation on Synergy Graphs}, author = {Filippo Bistaffa and Alessandro Farinelli and Jesus Cerquides and Juan Antonio Rodriguez-Aguilar and Sarvapali D Ramchurn}, url = {http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems}, pages = {13-20}, abstract = {We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents). |
2013 |
Alam, Muddasser; Alan, Alper; Rogers, Alex; Ramchurn, Sarvapali D Towards a smart home framework Inproceedings 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013), 2013. @inproceedings{eps357187, title = {Towards a smart home framework}, author = {Muddasser Alam and Alper Alan and Alex Rogers and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/357187/}, year = {2013}, date = {2013-01-01}, booktitle = {5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013)}, abstract = {We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work. |
Alam, Muddasser; Ramchurn, Sarvapali; Rogers, Alex Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013. @inproceedings{eps346637, title = {Cooperative energy exchange for the efficient use of energy and resources in remote communities. [Winner, Best Student Paper Award at AAMAS2013]}, author = {Muddasser Alam and Sarvapali Ramchurn and Alex Rogers}, url = {http://eprints.soton.ac.uk/346637/}, year = {2013}, date = {2013-01-01}, booktitle = {Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), 2013. @inproceedings{eps350941, title = {Interdependent multi-issue negotiation for energy exchange in remote communities}, author = {Muddasser Alam and Alex Rogers and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/350941/}, year = {2013}, date = {2013-01-01}, booktitle = {Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13)}, 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 negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%}, 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 negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37% |
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013. @inproceedings{eps357186, title = {Interdependent multi-issue negotiation for energy exchange in remote communities}, author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/357186/}, year = {2013}, date = {2013-01-01}, booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Publications
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. |
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. |
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. |
A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments Inproceedings International Joint Conference on Artificial Intelligence, 2015. |
Consumer Targeting in Residential Demand Response Programmes Inproceedings Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3. |
Multi-Agent Patrolling under Uncertainty and Threats Journal Article PLoS ONE, 10 (6), pp. e0130154, 2015, ISBN: 1932-6203. |
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article Cybernetics, IEEE Transactions on, PP (99), pp. 1-13, 2015, ISSN: 2168-2267. |
2014 |
Behavioural biometrics using electricity load profiles Journal Article Proceedings of the International Conference on Pattern Recognition, 2014. |
A field study of human-agent interaction for electricity tariff switching Inproceedings Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014. |
Doing the laundry with agents: a field trial of a future smart energy system in the home Inproceedings ACM CHI Conference on Human Factors in Computing Systems 2014, pp. 813–822, ACM 2014. |
BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees Inproceedings 13th International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2014. |
A message-passing approach to decentralised parallel machine scheduling Journal Article The Computer Journal, 2014. |
Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game. Inproceedings 11th Int. Conference on the Design of Cooperative Systems (COOP ?14), 2014. |
Social Implications of Agent-based Planning Support for Human Teams. Inproceedings 2014 Int. Conference on Collaboration Technologies and Systems, 2014. |
Coalition Structure Generation with the Graphics Processing Unit Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, 2014. |
Data quality assessment from provenance graphs Inproceedings Provenance Analytics 2014, 2014. |
On human-agent collectives Journal Article Communications of the ACM, 57 (12), pp. 33-42, 2014. |
Anytime Coalition Structure Generation on Synergy Graphs Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, pp. 13-20, 2014. |
2013 |
Towards a smart home framework Inproceedings 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013), 2013. |
Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013. |
Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), 2013. |
Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013. |
