2022 |
Soorati, Mohammad Divband; Gerding, Enrico; Marchioni, Enrico; Naumov, Pavel; Norman, Timothy; Ramchurn, Sarvapali; Rastegari, Baharak; Sobey, Adam; Stein, Sebastian; Tarapore, Danesh; Yazdanpanah, Vahid; Zhang, Jie From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems Journal Article AI Communications, 2022. @article{soton467975, title = {From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems}, author = {Mohammad Divband Soorati and Enrico Gerding and Enrico Marchioni and Pavel Naumov and Timothy Norman and Sarvapali Ramchurn and Baharak Rastegari and Adam Sobey and Sebastian Stein and Danesh Tarapore and Vahid Yazdanpanah and Jie Zhang}, url = {https://eprints.soton.ac.uk/467975/}, year = {2022}, date = {2022-07-01}, journal = {AI Communications}, abstract = {The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The Agents, Interaction and Complexity research group at the University of Southampton has a long track record of research in multiagent systems (MAS). We have made substantial scientific contributions across learning in MAS, game-theoretic techniques for coordinating agent systems, and formal methods for representation and reasoning. We highlight key results achieved by the group and elaborate on recent work and open research challenges in developing trustworthy autonomous systems and deploying human-centred AI systems that aim to support societal good. |
Bossens, David; Ramchurn, Sarvapali; Tarapore, Danesh Resilient robot teams: a review integrating decentralised control, change-detection, and learning Miscellaneous 2022. @misc{soton457101, title = {Resilient robot teams: a review integrating decentralised control, change-detection, and learning}, author = {David Bossens and Sarvapali Ramchurn and Danesh Tarapore}, url = {https://eprints.soton.ac.uk/457101/}, year = {2022}, date = {2022-06-01}, journal = {Current Robotics Reports}, abstract = {Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.ensuremath ensuremath Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.ensuremath ensuremath Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.}, keywords = {}, pubstate = {published}, tppubtype = {misc} } Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.ensuremath<br/ensuremath>ensuremath<br/ensuremath>Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.ensuremath<br/ensuremath>ensuremath<br/ensuremath>Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies. |
Early, Joseph; Bewley, Tom; Evers, Christine; Ramchurn, Sarvapali Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning Journal Article arXiv, 2022, (20 pages (9 main content; 2 references; 9 appendix). 11 figures (8 main content; 3 appendix)). @article{soton458023, title = {Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning}, author = {Joseph Early and Tom Bewley and Christine Evers and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/458023/}, year = {2022}, date = {2022-05-01}, journal = {arXiv}, abstract = {We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to include hidden state information that captures temporal dependencies in human assessment of trajectories. We then show how RM can be approached as a multiple instance learning (MIL) problem, and develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and that they provide interpretable learnt hidden information that can be used to train high-performing agent policies.}, note = {20 pages (9 main content; 2 references; 9 appendix). 11 figures (8 main content; 3 appendix)}, keywords = {}, pubstate = {published}, tppubtype = {article} } We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to include hidden state information that captures temporal dependencies in human assessment of trajectories. We then show how RM can be approached as a multiple instance learning (MIL) problem, and develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and that they provide interpretable learnt hidden information that can be used to train high-performing agent policies. |
Buermann, Jan; Georgiev, Dimitar; Gerding, Enrico; Hill, Lewis; Malik, Obaid; Pop, Alexandru; Pun, Matthew; Ramchurn, Sarvapali; Salisbury, Elliot; Stojanovic, Ivan An agent-based simulator for maritime transport decarbonisation: Demonstration track Inproceedings 21st International Conference on Autonomous Agents and Multiagent Systems (09/05/22 - 13/05/22), pp. 1890–1892, 2022. @inproceedings{soton456716, title = {An agent-based simulator for maritime transport decarbonisation: Demonstration track}, author = {Jan Buermann and Dimitar Georgiev and Enrico Gerding and Lewis Hill and Obaid Malik and Alexandru Pop and Matthew Pun and Sarvapali Ramchurn and Elliot Salisbury and Ivan Stojanovic}, url = {https://eprints.soton.ac.uk/456716/}, year = {2022}, date = {2022-05-01}, booktitle = {21st International Conference on Autonomous Agents and Multiagent Systems (09/05/22 - 13/05/22)}, pages = {1890--1892}, abstract = {Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator?s trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Greenhouse gas (GHG) emission reduction is an important and necessary goal; currently, different policies to reduce GHG emissions in maritime transport are being discussed. Amongst policies, like carbon taxes or carbon intensity targets, it is hard to determine which policies can successfully reduce GHG emissions while allowing the industry to be profitable. We introduce an agent-based maritime transport simulator to investigate the effectiveness of two decarbonisation policies by simulating a maritime transport operator?s trade pattern and fleet make-up changes as a reaction to taxation and fixed targets. This first of its kind simulator allows to compare and quantify the difference of carbon reduction policies and how they affect shipping operations. |
Rigas, Emmanouil S; Gerding, Enrico H; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article Energies, 15 (5), 2022, (Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.). @article{soton455806, title = {Mechanism design for efficient offline and online allocation of electric vehicles to charging stations}, author = {Emmanouil S Rigas and Enrico H Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades}, url = {https://eprints.soton.ac.uk/455806/}, year = {2022}, date = {2022-03-01}, journal = {Energies}, volume = {15}, number = {5}, abstract = {ensuremath note = {Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } ensuremath<pensuremath>The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total COensuremath<subensuremath>2ensuremath</subensuremath> emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey?Clark?Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs? aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs? preferences. In contrast, the fixed-price one was found to be vulnerable to agents? strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.ensuremath</pensuremath> |
Early, Joseph; Evers, Christine; Ramchurn, Sarvapali Model agnostic interpretability for multiple instance learning Inproceedings International Conference on Learning Representations 2022 (25/04/22 - 29/04/22), 2022, (25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements). @inproceedings{soton454952, title = {Model agnostic interpretability for multiple instance learning}, author = {Joseph Early and Christine Evers and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/454952/}, year = {2022}, date = {2022-01-01}, booktitle = {International Conference on Learning Representations 2022 (25/04/22 - 29/04/22)}, abstract = {In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems.}, note = {25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems. |
2021 |
Ramchurn, Sarvapali; Middleton, Stuart; McAuley, Derek; Webb, Helena; Hyde, Richard; Lisinska, Justyna A Response to Draft Online Safety Bill: A call for evidence from the Joint Committee Technical Report (10.18742/pub01-060), 2021. @techreport{soton451428, title = {A Response to Draft Online Safety Bill: A call for evidence from the Joint Committee}, author = {Sarvapali Ramchurn and Stuart Middleton and Derek McAuley and Helena Webb and Richard Hyde and Justyna Lisinska}, url = {https://eprints.soton.ac.uk/451428/}, year = {2021}, date = {2021-09-01}, number = {10.18742/pub01-060}, abstract = {This report is the Trustworthy Autonomous Hub (TAS-hub) response to the call for evidence from the Joint Committee on the Draft Online Safety Bill. The Joint Committee was established to consider the Government's draft Bill to establish a new regulatory framework to tackle harmful content online.}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } This report is the Trustworthy Autonomous Hub (TAS-hub) response to the call for evidence from the Joint Committee on the Draft Online Safety Bill. The Joint Committee was established to consider the Government's draft Bill to establish a new regulatory framework to tackle harmful content online. |
Ramchurn, Sarvapali; Stein, Sebastian; Jennings, Nicholas R Trustworthy human-AI partnerships Journal Article iScience, 24 (8), 2021. @article{soton450597, title = {Trustworthy human-AI partnerships}, author = {Sarvapali Ramchurn and Sebastian Stein and Nicholas R Jennings}, url = {https://eprints.soton.ac.uk/450597/}, year = {2021}, date = {2021-08-01}, journal = {iScience}, volume = {24}, number = {8}, abstract = {In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we foreground some of the key research challenges that arise in the design of trustworthy human-AI partnerships. In particular, we focus on the challenges in designing human-AI partnerships that need to be addressed to help humans and organisations trust their machine counterparts individually or as a collective (e.g., as robot teams or groups of software agents). We also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. By so doing, we will trigger new avenues of research that will address the key barriers to the adoption of AI-based systems more widely in our daily lives and in industry. |
Ramchurn, Sarvapali; Mousavi, Mohammad Reza; Toliyat, Seyed Mohammad Hossein; Kleinman, Mark; Lisinska, Justyna; Sempreboni, Diego; Stein, Sebastian; Gerding, Enrico; Gomer, Richard; DÁmore, Francesco The future of connected and automated mobility in the UK: call for evidence Technical Report (10.5258/SOTON/P0097), 2021, (The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King?s College London. The Hub sits at the centre of the pounds33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund. The role of the TAS Hub is to coordinate and work with six research nodes to establish a collaborative platform for the UK to enable the development of socially beneficial autonomous systems that are both trustworthy in principle and trusted in practice by individuals, society and government. Read more about the TAS Hub at https://www.tas.ac.uk/aboutus/overview/). @techreport{soton450228, title = {The future of connected and automated mobility in the UK: call for evidence}, author = {Sarvapali Ramchurn and Mohammad Reza Mousavi and Seyed Mohammad Hossein Toliyat and Mark Kleinman and Justyna Lisinska and Diego Sempreboni and Sebastian Stein and Enrico Gerding and Richard Gomer and Francesco DÁmore}, editor = {Wassim Dbouk}, url = {https://eprints.soton.ac.uk/450228/}, year = {2021}, date = {2021-07-01}, number = {10.5258/SOTON/P0097}, publisher = {University of Southampton}, abstract = {This report is a response to the call for evidence from the Department for Business, Energy & Industrial Strategy and the Centre for Connected and Autonomous Vehicles on the future of connected and automated mobility in the UK.ensuremath Executive Summary:Despite relative weaknesses in global collaboration and co-creation platforms, smart road and communication infrastructure, urban planning, and public awareness, the United Kingdom (UK) has a substantial strength in the area of Connected and Automated Mobility (CAM) by investing in research and innovation platforms for developing the underlying technologies, creating impact, and co-creation leading to innovative solutions. Many UK legal and policymaking initiatives in this domain are world leading. To sustain the UK?s leading position, we make the following recommendations:? The development of financial and policy-related incentive schemes for research and innovation in the foundations and applications of autonomous systems as well as schemes for proof of concepts, and commercialisation.? Supporting policy and standardisation initiatives as well as engagement and community-building activities to increase public awareness and trust.? Giving greater attention to integrating CAM/Connected Autonomous Shared Electric vehicles (CASE) policy with related government priorities for mobility, including supporting active transport and public transport, and improving air quality.? Further investment in updating liability and risk models and coming up with innovative liability schemes covering the Autonomous Vehicles (AVs) ecosystem.? Investing in training and retraining of the work force in the automotive, mobility, and transport sectors, particularly with skills concerningArtificial Intelligence (AI), software and computer systems, in order to ensure employability and an adequate response to the drastically changing industrial landscape}, note = {The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King?s College London. The Hub sits at the centre of the pounds33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund. The role of the TAS Hub is to coordinate and work with six research nodes to establish a collaborative platform for the UK to enable the development of socially beneficial autonomous systems that are both trustworthy in principle and trusted in practice by individuals, society and government. Read more about the TAS Hub at https://www.tas.ac.uk/aboutus/overview/}, keywords = {}, pubstate = {published}, tppubtype = {techreport} } This report is a response to the call for evidence from the Department for Business, Energy & Industrial Strategy and the Centre for Connected and Autonomous Vehicles on the future of connected and automated mobility in the UK.ensuremath<br/ensuremath>Executive Summary:Despite relative weaknesses in global collaboration and co-creation platforms, smart road and communication infrastructure, urban planning, and public awareness, the United Kingdom (UK) has a substantial strength in the area of Connected and Automated Mobility (CAM) by investing in research and innovation platforms for developing the underlying technologies, creating impact, and co-creation leading to innovative solutions. Many UK legal and policymaking initiatives in this domain are world leading. To sustain the UK?s leading position, we make the following recommendations:? The development of financial and policy-related incentive schemes for research and innovation in the foundations and applications of autonomous systems as well as schemes for proof of concepts, and commercialisation.? Supporting policy and standardisation initiatives as well as engagement and community-building activities to increase public awareness and trust.? Giving greater attention to integrating CAM/Connected Autonomous Shared Electric vehicles (CASE) policy with related government priorities for mobility, including supporting active transport and public transport, and improving air quality.? Further investment in updating liability and risk models and coming up with innovative liability schemes covering the Autonomous Vehicles (AVs) ecosystem.? Investing in training and retraining of the work force in the automotive, mobility, and transport sectors, particularly with skills concerningArtificial Intelligence (AI), software and computer systems, in order to ensure employability and an adequate response to the drastically changing industrial landscape |
Worrawichaipat, Phuriwat; Gerding, Enrico; Kaparias, Ioannis; Ramchurn, Sarvapali Resilient intersection management with multi-vehicle collision avoidance Journal Article Frontiers in Sustainable Cities, 3 , 2021. @article{soton449675, title = {Resilient intersection management with multi-vehicle collision avoidance}, author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/449675/}, year = {2021}, date = {2021-06-01}, journal = {Frontiers in Sustainable Cities}, volume = {3}, abstract = {In this paper, we propose a novel decentralised agent-based mechanism for road intersection management for connected autonomous vehicles. In our work we focus on road obstructions causing major traffic delays. In doing so, we propose the first decentralised mechanism able to maximise the overall vehicle throughput at intersections in the presence of obstructions. The distributed algorithm transfers most of the computational cost from the intersection manager to the driving agents, thereby improving scalability. Our realistic empirical experiments using SUMO show that, when an obstacle is located at the entrance or in the middle of the intersection, existing state of the art algorithms and traffic lights show a reduced throughput of 65?90% from the optimal point without obstructions while our mechanism can maintain the throughput upensuremath Q7 to 94?99%.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we propose a novel decentralised agent-based mechanism for road intersection management for connected autonomous vehicles. In our work we focus on road obstructions causing major traffic delays. In doing so, we propose the first decentralised mechanism able to maximise the overall vehicle throughput at intersections in the presence of obstructions. The distributed algorithm transfers most of the computational cost from the intersection manager to the driving agents, thereby improving scalability. Our realistic empirical experiments using SUMO show that, when an obstacle is located at the entrance or in the middle of the intersection, existing state of the art algorithms and traffic lights show a reduced throughput of 65?90% from the optimal point without obstructions while our mechanism can maintain the throughput upensuremath<br/ensuremath>Q7 to 94?99%. |
Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali Large-scale, dynamic and distributed coalition formation with spatial and†temporal constraints Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 108–125, 2021. @article{soton452050, title = {Large-scale, dynamic and distributed coalition formation with spatial and†temporal constraints}, author = {Luca Capezzuto and Danesh Tarapore and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/452050/}, year = {2021}, date = {2021-05-01}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, pages = {108--125}, abstract = {The†Coalition Formation with Spatial and Temporal constraints Problem†(CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up†to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes†3.79%$pm$[42.22%,1.96%]†more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.ensuremath }, keywords = {}, pubstate = {published}, tppubtype = {article} } The†Coalition Formation with Spatial and Temporal constraints Problem†(CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up†to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes†3.79%$pm$[42.22%,1.96%]†more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.ensuremath<br/ensuremath> |
Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali D Anytime and efficient multi-agent coordination for disaster response Journal Article SN Computer Science, 2 (3), 2021. @article{soton467373, title = {Anytime and efficient multi-agent coordination for disaster response}, author = {Luca Capezzuto and Danesh Tarapore and Sarvapali D Ramchurn}, url = {https://eprints.soton.ac.uk/467373/}, year = {2021}, date = {2021-03-01}, journal = {SN Computer Science}, volume = {2}, number = {3}, abstract = {The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (CFLA2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. CFLA2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (CFLA2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in CFLA2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. CFLA2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP. |
Ortega, Andre P; Ramchurn, Sarvapali; Tran-Thanh, Long; Merrett, Geoff Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach Journal Article Ad Hoc Networks, 112 , 2021. @article{soton445733, title = {Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach}, author = {Andre P Ortega and Sarvapali Ramchurn and Long Tran-Thanh and Geoff Merrett}, url = {https://eprints.soton.ac.uk/445733/}, year = {2021}, date = {2021-03-01}, journal = {Ad Hoc Networks}, volume = {112}, abstract = {The proliferation of ?Things? over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The proliferation of ?Things? over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work. |
Yazdanpanah, Vahid; Gerding, Enrico H; Stein, Sebastian; Dastani, Mehdi; Jonker, Catholijn M; Norman, Timothy J; Ramchurn, Sarvapali D Responsibility ascription in trustworthy autonomous systems Inproceedings Embedding AI in Society (18/02/21 - 19/02/21), 2021. @inproceedings{soton446459, title = {Responsibility ascription in trustworthy autonomous systems}, author = {Vahid Yazdanpanah and Enrico H Gerding and Sebastian Stein and Mehdi Dastani and Catholijn M Jonker and Timothy J Norman and Sarvapali D Ramchurn}, url = {https://eprints.soton.ac.uk/446459/}, year = {2021}, date = {2021-02-01}, booktitle = {Embedding AI in Society (18/02/21 - 19/02/21)}, abstract = {To develop and effectively deploy Trustworthy Autonomous Systems (TAS), we face various social, technological, legal, and ethical challenges in which different notions of responsibility can play a key role. In this work, we elaborate on these challenges, discuss research gaps, and show how the multidimensional notion of responsibility can play a key role to bridge them. We argue that TAS requires operational tools to represent and reason about the responsibilities of humans as well as AI agents.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } To develop and effectively deploy Trustworthy Autonomous Systems (TAS), we face various social, technological, legal, and ethical challenges in which different notions of responsibility can play a key role. In this work, we elaborate on these challenges, discuss research gaps, and show how the multidimensional notion of responsibility can play a key role to bridge them. We argue that TAS requires operational tools to represent and reason about the responsibilities of humans as well as AI agents. |
Beal, Ryan James; Middleton, Stuart; Norman, Timothy; Ramchurn, Sarvapali Combining machine learning and human experts to predict match outcomes in football: A baseline model Inproceedings The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (02/02/21 - 09/02/21), 2021. @inproceedings{soton445607, title = {Combining machine learning and human experts to predict match outcomes in football: A baseline model}, author = {Ryan James Beal and Stuart Middleton and Timothy Norman and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/445607/}, year = {2021}, date = {2021-02-01}, booktitle = {The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (02/02/21 - 09/02/21)}, abstract = {In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods. |
Lhopital, Sacha; Aknine, Samir; Ramchurn, Sarvapali; Thavonekham, Vincent; Vu, Huan Decentralised control of intelligent devices: a healthcare facility study Inproceedings Bassiliades, Nick; Chalkiadakis, Georgios; de Jonge, Dave (Ed.): Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers, pp. 20–36, Springer, 2021. @inproceedings{soton447983, title = {Decentralised control of intelligent devices: a healthcare facility study}, author = {Sacha Lhopital and Samir Aknine and Sarvapali Ramchurn and Vincent Thavonekham and Huan Vu}, editor = {Nick Bassiliades and Georgios Chalkiadakis and Dave de Jonge}, url = {https://eprints.soton.ac.uk/447983/}, year = {2021}, date = {2021-01-01}, booktitle = {Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers}, volume = {12520 LNAI}, pages = {20--36}, publisher = {Springer}, abstract = {ensuremath keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } ensuremath<pensuremath>We present a novel approach to the management of notifications from devices in a healthcare setting. We employ a distributed constraint optimisation (DCOP) approach to the delivery of notification for healthcare assistants that aims to preserve the privacy of patients while reducing the intrusiveness of such notifications. Our approach reduces the workload of the assistants and improves patient safety by automating task allocation while ensuring high priority needs are addressed in a timely manner. We propose and evaluate several DCOP models both in simulation and in real-world deployments. Our models are shown to be efficient both in terms of computation and communication costs.ensuremath</pensuremath> |
Ryan, James Beal; Chalkiadakis, Georgios; Norman, Timothy; Ramchurn, Sarvapali Optimising long-term outcomes using real-world fluent objectives: an application to football Inproceedings 20th International Conference on Autonomous Agents and Multiagent Systems (03/05/21 - 07/05/21), pp. 196–204, 2021. @inproceedings{soton449655, title = {Optimising long-term outcomes using real-world fluent objectives: an application to football}, author = {James Beal Ryan and Georgios Chalkiadakis and Timothy Norman and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/449655/}, year = {2021}, date = {2021-01-01}, booktitle = {20th International Conference on Autonomous Agents and Multiagent Systems (03/05/21 - 07/05/21)}, pages = {196--204}, abstract = {In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. ensuremath We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams? long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment across a given time frame. ensuremath<br/ensuremath>We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment and increase the teams? long-term performance. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%. |
Merhej, Charbel; Ryan, James Beal; Matthews, Tim; Ramchurn, Sarvapali What happened next? Using deep learning to value defensive actions in football event-data Inproceedings KDD 2021 (14/08/21 - 18/08/21), pp. 3394–3403, 2021. @inproceedings{soton449656, title = {What happened next? Using deep learning to value defensive actions in football event-data}, author = {Charbel Merhej and James Beal Ryan and Tim Matthews and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/449656/}, year = {2021}, date = {2021-01-01}, booktitle = {KDD 2021 (14/08/21 - 18/08/21)}, pages = {3394--3403}, abstract = {Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data. |
2020 |
Beal, Ryan James; Norman, Timothy; Ramchurn, Sarvapali Optimising daily fantasy sports teams with artificial intelligence Journal Article International Journal of Computer Science in Sport, 19 (2), 2020. @article{soton445995, title = {Optimising daily fantasy sports teams with artificial intelligence}, author = {Ryan James Beal and Timothy Norman and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/445995/}, year = {2020}, date = {2020-12-01}, journal = {International Journal of Computer Science in Sport}, volume = {19}, number = {2}, abstract = {This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season |
Beal, Ryan James; Norman, Timothy; Ramchurn, Sarvapali A critical comparison of machine learning classifiers to predict match outcomes in the NFL Journal Article International Journal of Computer Science in Sport, 19 (2), 2020. @article{soton446078, title = {A critical comparison of machine learning classifiers to predict match outcomes in the NFL}, author = {Ryan James Beal and Timothy Norman and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/446078/}, year = {2020}, date = {2020-12-01}, journal = {International Journal of Computer Science in Sport}, volume = {19}, number = {2}, abstract = {In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Na"ive Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a Na"ive Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers. |
Rigas, Emmanouil S; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick Mechanism design for efficient allocation of electric vehicles to charging stations Inproceedings SETN 2020: 11th Hellenic Conference on Artificial Intelligence, pp. 10–15, 2020. @inproceedings{soton446412, title = {Mechanism design for efficient allocation of electric vehicles to charging stations}, author = {Emmanouil S Rigas and Enrico Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades}, url = {https://eprints.soton.ac.uk/446412/}, year = {2020}, date = {2020-09-01}, booktitle = {SETN 2020: 11th Hellenic Conference on Artificial Intelligence}, pages = {10--15}, abstract = {The electrification of transport can significantly reduce CO2 emissions and their negative impact on the environment. In this paper, we study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop an offline solution that treats EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixed-price one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism. We observe that the VCG mechanism services on average 1.5% more EVs than the fixed-price one. In addition, when the stations get congested, VCG leads to higher prices for the EVs and higher profit for the stations, but lower utility for the EVs. However, the VCG mechanism guarantees truthful reporting of the EVs? preferences.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The electrification of transport can significantly reduce CO2 emissions and their negative impact on the environment. In this paper, we study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop an offline solution that treats EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixed-price one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism. We observe that the VCG mechanism services on average 1.5% more EVs than the fixed-price one. In addition, when the stations get congested, VCG leads to higher prices for the EVs and higher profit for the stations, but lower utility for the EVs. However, the VCG mechanism guarantees truthful reporting of the EVs? preferences. |
Oluwasuji, Olabambo Ifeoluwa; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Dyanand Solving the fair electric load shedding problem in developing countries Journal Article Autonomous Agents and Multi-Agent Systems, 34 (1), pp. 12, 2020. @article{oluwasuji2020solving, title = {Solving the fair electric load shedding problem in developing countries}, author = {Olabambo Ifeoluwa Oluwasuji and Obaid Malik and Jie Zhang and Sarvapali Dyanand Ramchurn}, year = {2020}, date = {2020-01-01}, journal = {Autonomous Agents and Multi-Agent Systems}, volume = {34}, number = {1}, pages = {12}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Oluwasuji, Olabambo Ifeoluwa; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Dyanand Solving the fair electric load shedding problem in developing countries Journal Article Auton. Agents Multi Agent Syst., 34 (1), pp. 12, 2020. @article{DBLP:journals/aamas/OluwasujiMZR20, title = {Solving the fair electric load shedding problem in developing countries}, author = {Olabambo Ifeoluwa Oluwasuji and Obaid Malik and Jie Zhang and Sarvapali Dyanand Ramchurn}, url = {https://doi.org/10.1007/s10458-019-09428-8}, doi = {10.1007/s10458-019-09428-8}, year = {2020}, date = {2020-01-01}, journal = {Auton. Agents Multi Agent Syst.}, volume = {34}, number = {1}, pages = {12}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Seitaridis, Andreas; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations Journal Article Simul. Model. Pract. Theory, 100 , pp. 102040, 2020. @article{DBLP:journals/simpra/SeitaridisRBR20, title = {An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations}, author = {Andreas Seitaridis and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn}, url = {https://doi.org/10.1016/j.simpat.2019.102040}, doi = {10.1016/j.simpat.2019.102040}, year = {2020}, date = {2020-01-01}, journal = {Simul. Model. Pract. Theory}, volume = {100}, pages = {102040}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Koufakis, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer Journal Article IEEE Trans. Intell. Transp. Syst., 21 (5), pp. 2128–2138, 2020. @article{DBLP:journals/tits/KoufakisRBR20, title = {Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer}, author = {Alexandros Koufakis and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn}, url = {https://doi.org/10.1109/TITS.2019.2914087}, doi = {10.1109/TITS.2019.2914087}, year = {2020}, date = {2020-01-01}, journal = {IEEE Trans. Intell. Transp. Syst.}, volume = {21}, number = {5}, pages = {2128--2138}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Beal, Ryan; Changder, Narayan; Norman, Timothy D; Ramchurn, Sarvapali D Learning the Value of Teamwork to Form Efficient Teams Inproceedings The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 7063–7070, AAAI Press, 2020. @inproceedings{DBLP:conf/aaai/BealCNR20, title = {Learning the Value of Teamwork to Form Efficient Teams}, author = {Ryan Beal and Narayan Changder and Timothy D Norman and Sarvapali D Ramchurn}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6192}, year = {2020}, date = {2020-01-01}, booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020}, pages = {7063--7070}, publisher = {AAAI Press}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Changder, Narayan; Aknine, Samir; Ramchurn, Sarvapali D; Dutta, Animesh ODSS: Efficient Hybridization for Optimal Coalition Structure Generation Inproceedings The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 7079–7086, AAAI Press, 2020. @inproceedings{DBLP:conf/aaai/ChangderARD20, title = {ODSS: Efficient Hybridization for Optimal Coalition Structure Generation}, author = {Narayan Changder and Samir Aknine and Sarvapali D Ramchurn and Animesh Dutta}, url = {https://aaai.org/ojs/index.php/AAAI/article/view/6194}, year = {2020}, date = {2020-01-01}, booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020}, pages = {7079--7086}, publisher = {AAAI Press}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Beal, Ryan; Chalkiadakis, Georgios; Norman, Timothy J; Ramchurn, Sarvapali D Optimising Game Tactics for Football Inproceedings Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 141–149, International Foundation for Autonomous Agents and Multiagent Systems, 2020. @inproceedings{DBLP:conf/atal/BealCNR20, title = {Optimising Game Tactics for Football}, author = {Ryan Beal and Georgios Chalkiadakis and Timothy J Norman and Sarvapali D Ramchurn}, editor = {Amal El Fallah Seghrouchni and Gita Sukthankar and Bo An and Neil Yorke -}, url = {https://dl.acm.org/doi/abs/10.5555/3398761.3398783}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020}, pages = {141--149}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Oluwasuji, Olabambo I; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali D Solving the Fair Electric Load Shedding Problem in Developing Countries Inproceedings Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 2155–2157, International Foundation for Autonomous Agents and Multiagent Systems, 2020. @inproceedings{DBLP:conf/atal/OluwasujiM0R20, title = {Solving the Fair Electric Load Shedding Problem in Developing Countries}, author = {Olabambo I Oluwasuji and Obaid Malik and Jie Zhang and Sarvapali D Ramchurn}, editor = {Amal El Fallah Seghrouchni and Gita Sukthankar and Bo An and Neil Yorke -}, url = {https://dl.acm.org/doi/abs/10.5555/3398761.3399108}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020}, pages = {2155--2157}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Wu, Feng; Ramchurn, Sarvapali D Monte-Carlo Tree Search for Scalable Coalition Formation Inproceedings Bessiere, Christian (Ed.): Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 407–413, ijcai.org, 2020. @inproceedings{DBLP:conf/ijcai/0001R20a, title = {Monte-Carlo Tree Search for Scalable Coalition Formation}, author = {Feng Wu and Sarvapali D Ramchurn}, editor = {Christian Bessiere}, url = {https://doi.org/10.24963/ijcai.2020/57}, doi = {10.24963/ijcai.2020/57}, year = {2020}, date = {2020-01-01}, booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020}, pages = {407--413}, publisher = {ijcai.org}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rigas, Emmanouil S; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick Mechanism design for efficient allocation of electric vehicles to charging stations Inproceedings Spyropoulos, Constantine D; Varlamis, Iraklis; Androutsopoulos, Ion; Malakasiotis, Prodromos (Ed.): SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, September 2-4, 2020, pp. 10–15, ACM, 2020. @inproceedings{DBLP:conf/setn/RigasG0RB20, title = {Mechanism design for efficient allocation of electric vehicles to charging stations}, author = {Emmanouil S Rigas and Enrico Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades}, editor = {Constantine D Spyropoulos and Iraklis Varlamis and Ion Androutsopoulos and Prodromos Malakasiotis}, url = {https://dl.acm.org/doi/10.1145/3411408.3411434}, year = {2020}, date = {2020-01-01}, booktitle = {SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, September 2-4, 2020}, pages = {10--15}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Beal, Ryan; Chalkiadakis, Georgios; Norman, Timothy J; Ramchurn, Sarvapali D Optimising Game Tactics for Football Journal Article CoRR, abs/2003.10294 , 2020. @article{DBLP:journals/corr/abs-2003-10294, title = {Optimising Game Tactics for Football}, author = {Ryan Beal and Georgios Chalkiadakis and Timothy J Norman and Sarvapali D Ramchurn}, url = {https://arxiv.org/abs/2003.10294}, year = {2020}, date = {2020-01-01}, journal = {CoRR}, volume = {abs/2003.10294}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Capezzuto, Luca; Tarapore, Danesh; Ramchurn, Sarvapali D Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints Journal Article CoRR, abs/2003.13806 , 2020. @article{DBLP:journals/corr/abs-2003-13806, title = {Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints}, author = {Luca Capezzuto and Danesh Tarapore and Sarvapali D Ramchurn}, url = {https://arxiv.org/abs/2003.13806}, year = {2020}, date = {2020-01-01}, journal = {CoRR}, volume = {abs/2003.13806}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Rigas, Emmanouil; Gerding, Enrico; Stein, Sebastian; Ramchurn, Sarvapali D; Bassiliades, Nick Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations Journal Article CoRR, abs/2007.09715 , 2020. @article{DBLP:journals/corr/abs-2007-09715, title = {Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations}, author = {Emmanouil Rigas and Enrico Gerding and Sebastian Stein and Sarvapali D Ramchurn and Nick Bassiliades}, url = {https://arxiv.org/abs/2007.09715}, year = {2020}, date = {2020-01-01}, journal = {CoRR}, volume = {abs/2007.09715}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2019 |
Abeywickrama, Dhaminda B; Cirstea, Corina; Ramchurn, Sarvapali D Model Checking Human-Agent Collectives for Responsible AI Inproceedings 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1–8, IEEE 2019. @inproceedings{abeywickrama2019model, title = {Model Checking Human-Agent Collectives for Responsible AI}, author = {Dhaminda B Abeywickrama and Corina Cirstea and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, booktitle = {2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}, pages = {1--8}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Beal, Ryan; Norman, Timothy J; Ramchurn, Sarvapali D Artificial intelligence for team sports: a survey Journal Article The Knowledge Engineering Review, 34 , 2019. @article{beal2019artificial, title = {Artificial intelligence for team sports: a survey}, author = {Ryan Beal and Timothy J Norman and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, journal = {The Knowledge Engineering Review}, volume = {34}, publisher = {Cambridge University Press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Abioye, Ayodeji Opeyemi; Prior, Stephen D; Thomas, Glyn T; Saddington, Peter; Ramchurn, Sarvapali D Multimodal human aerobotic interaction Incollection Unmanned Aerial Vehicles: Breakthroughs in Research and Practice, pp. 142–165, IGI Global, 2019. @incollection{abioye2019multimodal, title = {Multimodal human aerobotic interaction}, author = {Ayodeji Opeyemi Abioye and Stephen D Prior and Glyn T Thomas and Peter Saddington and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, booktitle = {Unmanned Aerial Vehicles: Breakthroughs in Research and Practice}, pages = {142--165}, publisher = {IGI Global}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Fuentes, Carolina; Porcheron, Martin; Fischer, Joel E; Costanza, Enrico; Malilk, Obaid; Ramchurn, Sarvapali D Tracking the Consumption of Home Essentials Inproceedings Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 639, ACM 2019. @inproceedings{fuentes2019tracking, title = {Tracking the Consumption of Home Essentials}, author = {Carolina Fuentes and Martin Porcheron and Joel E Fischer and Enrico Costanza and Obaid Malilk and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems}, pages = {639}, organization = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Koufakis, Alexandros-Michail; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer Journal Article IEEE Transactions on Intelligent Transportation Systems, 2019. @article{koufakis2019offline, title = {Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer}, author = {Alexandros-Michail Koufakis and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, journal = {IEEE Transactions on Intelligent Transportation Systems}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Seitaridis, Andreas; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D An Agent-based Negotiation Scheme for the Distribution of Electric Vehicles Across a Set of Charging Stations Journal Article Simulation Modelling Practice and Theory, pp. 102040, 2019. @article{seitaridis2019agent, title = {An Agent-based Negotiation Scheme for the Distribution of Electric Vehicles Across a Set of Charging Stations}, author = {Andreas Seitaridis and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, journal = {Simulation Modelling Practice and Theory}, pages = {102040}, publisher = {Elsevier}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Abioye, Ayodeji Opeyemi; Prior, Stephen D; Saddington, Peter; Ramchurn, Sarvapali D Effects of Varying Noise Levels and Lighting Levels on Multimodal Speech and Visual Gesture Interaction with Aerobots Journal Article Applied Sciences, 9 (10), pp. 2066, 2019. @article{abioye2019effects, title = {Effects of Varying Noise Levels and Lighting Levels on Multimodal Speech and Visual Gesture Interaction with Aerobots}, author = {Ayodeji Opeyemi Abioye and Stephen D Prior and Peter Saddington and Sarvapali D Ramchurn}, year = {2019}, date = {2019-01-01}, journal = {Applied Sciences}, volume = {9}, number = {10}, pages = {2066}, publisher = {Multidisciplinary Digital Publishing Institute}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2018 |
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article Artificial Intelligence, 262 , pp. 248–278, 2018. @article{soton422097, title = {Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes}, author = {Emmanouil Rigas and Sarvapali Ramchurn and Nick Bassiliades}, url = {https://eprints.soton.ac.uk/422097/}, year = {2018}, date = {2018-09-01}, journal = {Artificial Intelligence}, volume = {262}, pages = {248--278}, abstract = {We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pick-up and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is used. When some EVs do not have a large enough battery to execute some of the longest trips, the incremental-MIP generates solutions slightly better than the greedy, while the optimal algorithm is the best but scales up to medium sized problems only. Moreover, the online algorithm is shown to be on average at least 90% of the optimal. Finally, the greedy algorithm scales to 10-times more tasks than the incremental-MIP and 1000-times more than the static MIP in reasonable time.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pick-up and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is used. When some EVs do not have a large enough battery to execute some of the longest trips, the incremental-MIP generates solutions slightly better than the greedy, while the optimal algorithm is the best but scales up to medium sized problems only. Moreover, the online algorithm is shown to be on average at least 90% of the optimal. Finally, the greedy algorithm scales to 10-times more tasks than the incremental-MIP and 1000-times more than the static MIP in reasonable time. |
Ayodeji, Opeyemi Abioye; Prior, Stephen; Thomas, Trevor; Saddington, Peter; Ramchurn, Sarvapali D The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot Inproceedings 19th Towards Autonomous Robotic Systems (TAROS) Conference 2018, pp. 423–437, Springer, 2018. @inproceedings{soton418869, title = {The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot}, author = {Opeyemi Abioye Ayodeji and Stephen Prior and Trevor Thomas and Peter Saddington and Sarvapali D. Ramchurn}, url = {https://eprints.soton.ac.uk/418869/}, year = {2018}, date = {2018-07-01}, booktitle = {19th Towards Autonomous Robotic Systems (TAROS) Conference 2018}, volume = {10965}, pages = {423--437}, publisher = {Springer}, abstract = {This paper describes a model of the multimodal speech and visual gesture (mSVG) control for aerobots operating at higher nCA autonomy levels, within the context of a patrol, search, and rescue application. The developed mSVG control architecture, its mathematical navigation model, and some high level command operation models were discussed. This was successfully tested using both MATLAB simulation and python based ROS Gazebo UAV simulations. Some limitations were identified, which formed the basis for the further works presented.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper describes a model of the multimodal speech and visual gesture (mSVG) control for aerobots operating at higher nCA autonomy levels, within the context of a patrol, search, and rescue application. The developed mSVG control architecture, its mathematical navigation model, and some high level command operation models were discussed. This was successfully tested using both MATLAB simulation and python based ROS Gazebo UAV simulations. Some limitations were identified, which formed the basis for the further works presented. |
Ortega, Andre P; Merrett, Geoff; Ramchurn, Sarvapali Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks Inproceedings 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18), 2018. @inproceedings{soton423060, title = {Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks}, author = {Andre P Ortega and Geoff Merrett and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/423060/}, year = {2018}, date = {2018-07-01}, booktitle = {2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18)}, abstract = {As the Internet of Things grows, the number of wireless sensor networks deployed in close proximity will continue to increase. By nature, these networks are limited by the battery supply that determines their lifetime and system utility. To counter such a shortcoming, energy harvesting technologies have become increasingly investigated to provide a perpetual energy source; however, new problems arise as a result of their wide spatio-temporal variation. In this paper, we propose opportunistic energy trading, which enables otherwise independent networks to be sustained by sharing resources. Our goal is to provide a novel cooperation model based on negotiation to solve coordination conflicts between energy harvesting wireless sensor networks. Results show that networks are able to satisfy their loads when they agree to cooperate.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } As the Internet of Things grows, the number of wireless sensor networks deployed in close proximity will continue to increase. By nature, these networks are limited by the battery supply that determines their lifetime and system utility. To counter such a shortcoming, energy harvesting technologies have become increasingly investigated to provide a perpetual energy source; however, new problems arise as a result of their wide spatio-temporal variation. In this paper, we propose opportunistic energy trading, which enables otherwise independent networks to be sustained by sharing resources. Our goal is to provide a novel cooperation model based on negotiation to solve coordination conflicts between energy harvesting wireless sensor networks. Results show that networks are able to satisfy their loads when they agree to cooperate. |
Ayodeji, Opeyemi Abioye; Prior, Stephen; Thomas, Trevor; Saddington, Peter; Ramchurn, Sarvapali IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018, pp. 842–845, IEEE, 2018. @inproceedings{soton418871, title = {Quantifying the effects of varying light-visibility and noise-sound levels in practical multimodal speech and visual gesture (mSVG) interaction with aerobots}, author = {Opeyemi Abioye Ayodeji and Stephen Prior and Trevor Thomas and Peter Saddington and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/418871/}, year = {2018}, date = {2018-06-01}, booktitle = {IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018}, pages = {842--845}, publisher = {IEEE}, abstract = {This paper discusses the research work conducted to quantify the effective range of lighting levels and ambient noise levels in order to inform the design and development of a multimodal speech and visual gesture (mSVG) control interface for the control of a UAV. Noise level variation from 55 dB to 85 dB is observed under control lab conditions to determine where speech commands for a UAV fails, and to consider why, and possibly suggest a solution around this. Similarly, lighting levels are varied within the control lab condition to determine a range of effective visibility levels. The limitation of this work and some further work from this were also presented.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper discusses the research work conducted to quantify the effective range of lighting levels and ambient noise levels in order to inform the design and development of a multimodal speech and visual gesture (mSVG) control interface for the control of a UAV. Noise level variation from 55 dB to 85 dB is observed under control lab conditions to determine where speech commands for a UAV fails, and to consider why, and possibly suggest a solution around this. Similarly, lighting levels are varied within the control lab condition to determine a range of effective visibility levels. The limitation of this work and some further work from this were also presented. |
Garcia, Pedro Garcia; Costanza, Enrico; Verame, Jhim; Nowacka, Diana; Ramchurn, Sarvapali D Seeing (movement) is believing: the effect of motion on perception of automatic systems performance Journal Article Human-Computer Interaction, pp. 1-51, 2018. @article{soton422967, title = {Seeing (movement) is believing: the effect of motion on perception of automatic systems performance}, author = {Pedro Garcia Garcia and Enrico Costanza and Jhim Verame and Diana Nowacka and Sarvapali D Ramchurn}, url = {https://eprints.soton.ac.uk/422967/}, year = {2018}, date = {2018-04-01}, journal = {Human-Computer Interaction}, pages = {1-51}, abstract = {ensuremath keywords = {}, pubstate = {published}, tppubtype = {article} } ensuremath<pensuremath>In this article, we report on one lab study and seven follow-up studies on a crowdsourcing platform designed to investigate the potential of animation cues to influence users? perception of two smart systems: a handwriting recognition and a part-of-speech tagging system. Results from the first three studies indicate that animation cues can influence a participant?s perception of both systems? performance. The subsequent three studies, designed to try and identify an explanation for this effect, suggest that this effect is related to the participants? mental model of the smart system. The last two studies were designed to characterize the effect more in detail, and they revealed that different amounts of animation do not seem to create substantial differences and that the effect persists even when the system?s performance decreases, but only when the difference in performance level between the systems being compared is small.ensuremath</pensuremath> |
Bicego, M; Farinelli, A; Grosso, E; Paolini, D; Ramchurn, S D On the distinctiveness of the electricity load profile Journal Article Pattern Recognition, 74 (Supplement C), pp. 317–325, 2018, ISSN: 0031-3203. @article{BICEGO2018317b, title = {On the distinctiveness of the electricity load profile}, author = {M. Bicego and A. Farinelli and E. Grosso and D. Paolini and S.D. Ramchurn}, url = {http://www.sciencedirect.com/science/article/pii/S0031320317303904}, doi = {https://doi.org/10.1016/j.patcog.2017.09.039}, issn = {0031-3203}, year = {2018}, date = {2018-01-01}, journal = {Pattern Recognition}, volume = {74}, number = {Supplement C}, pages = {317--325}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Olabambo, Ifeoluwa Oluwasuji; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Algorithms for fair load shedding in developing countries Inproceedings Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1590-1596, 2018. @inproceedings{soton420541, title = {Algorithms for fair load shedding in developing countries}, author = {Ifeoluwa Oluwasuji Olabambo and Obaid Malik and Jie Zhang and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/420541/}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {1590-1596}, abstract = {Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. During load shedding, many homes are left without electricity, causing them inconvenience and discomfort. In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity. We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. During load shedding, many homes are left without electricity, causing them inconvenience and discomfort. In this paper, we present a number of optimization heuristics that focus on pairwise and groupwise fairness, such that households (i.e. agents) are fairly allocated electricity. We evaluate the heuristics against standard fairness metrics in terms of comfort delivered to homes, as well as the number of times they are disconnected from electricity supply. Thus, we establish new benchmarks for fair load shedding schemes. |
Olabambo, Ifeoluwa Oluwasuji; Malik, Obaid; Zhang, Jie; Ramchurn, Sarvapali Algorithms to manage load shedding events in developing countries Inproceedings Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS), pp. 2034-2036, 2018. @inproceedings{soton420127, title = {Algorithms to manage load shedding events in developing countries}, author = {Ifeoluwa Oluwasuji Olabambo and Obaid Malik and Jie Zhang and Sarvapali Ramchurn}, url = {https://eprints.soton.ac.uk/420127/}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS)}, pages = {2034-2036}, abstract = {Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves homes without electricity, causing them discomfort and inconvenience. Because fairness is not a priority when shedding load, some homes bear the brunt of these effects. In this paper, we present our ongoing research into considering fairness when shedding load at the household level.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Due to the limited generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves homes without electricity, causing them discomfort and inconvenience. Because fairness is not a priority when shedding load, some homes bear the brunt of these effects. In this paper, we present our ongoing research into considering fairness when shedding load at the household level. |
Kiel, Manzano Verame Jhim; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Crabtree, Andy; Rodden, Tom; Jennings, Nick Learning from the veg box: Designing unpredictability in agency delegation Inproceedings CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018. @inproceedings{soton417370, title = {Learning from the veg box: Designing unpredictability in agency delegation}, author = {Manzano Verame Jhim Kiel and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Andy Crabtree and Tom Rodden and Nick Jennings}, url = {https://eprints.soton.ac.uk/417370/}, year = {2018}, date = {2018-01-01}, booktitle = {CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems}, publisher = {ACM}, abstract = {The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semistructured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people?s diverse values, and their coordinative and creative practices.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semistructured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people?s diverse values, and their coordinative and creative practices. |
Publications
2022 |
From Intelligent Agents to Trustworthy Human-Centred Multiagent Systems Journal Article AI Communications, 2022. |
Resilient robot teams: a review integrating decentralised control, change-detection, and learning Miscellaneous 2022. |
Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning Journal Article arXiv, 2022, (20 pages (9 main content; 2 references; 9 appendix). 11 figures (8 main content; 3 appendix)). |
An agent-based simulator for maritime transport decarbonisation: Demonstration track Inproceedings 21st International Conference on Autonomous Agents and Multiagent Systems (09/05/22 - 13/05/22), pp. 1890–1892, 2022. |
Mechanism design for efficient offline and online allocation of electric vehicles to charging stations Journal Article Energies, 15 (5), 2022, (Funding Information: Funding: This research study was co-financed by Greece and the European Union (European Social Fund--ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Reinforcement of Postdoctoral Researchers-2nd Cycle? (MIS-5033021), implemented by State Scholarships Foundation (IKY). Copyright 2022 Elsevier B.V., All rights reserved.). |
Model agnostic interpretability for multiple instance learning Inproceedings International Conference on Learning Representations 2022 (25/04/22 - 29/04/22), 2022, (25 pages (9 content, 2 acknowledgement + references, 14 appendix). 16 figures (3 main content, 13 appendix). Submitted and accepted to ICLR 22, see http://openreview.net/forum?id=KSSfF5lMIAg . Revision: added additional acknowledgements). |
2021 |
A Response to Draft Online Safety Bill: A call for evidence from the Joint Committee Technical Report (10.18742/pub01-060), 2021. |
Trustworthy human-AI partnerships Journal Article iScience, 24 (8), 2021. |
The future of connected and automated mobility in the UK: call for evidence Technical Report (10.5258/SOTON/P0097), 2021, (The UKRI TAS Hub assembles a team from the Universities of Southampton, Nottingham and King?s College London. The Hub sits at the centre of the pounds33M Trustworthy Autonomous Systems Programme, funded by the UKRI Strategic Priorities Fund. The role of the TAS Hub is to coordinate and work with six research nodes to establish a collaborative platform for the UK to enable the development of socially beneficial autonomous systems that are both trustworthy in principle and trusted in practice by individuals, society and government. Read more about the TAS Hub at https://www.tas.ac.uk/aboutus/overview/). |
Resilient intersection management with multi-vehicle collision avoidance Journal Article Frontiers in Sustainable Cities, 3 , 2021. |
Large-scale, dynamic and distributed coalition formation with spatial and†temporal constraints Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 108–125, 2021. |
Anytime and efficient multi-agent coordination for disaster response Journal Article SN Computer Science, 2 (3), 2021. |
Partner selection in self-organised wireless sensor networks for opportunistic energy negotiation: A multi-armed bandit based approach Journal Article Ad Hoc Networks, 112 , 2021. |
Responsibility ascription in trustworthy autonomous systems Inproceedings Embedding AI in Society (18/02/21 - 19/02/21), 2021. |
Combining machine learning and human experts to predict match outcomes in football: A baseline model Inproceedings The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (02/02/21 - 09/02/21), 2021. |
Decentralised control of intelligent devices: a healthcare facility study Inproceedings Bassiliades, Nick; Chalkiadakis, Georgios; de Jonge, Dave (Ed.): Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers, pp. 20–36, Springer, 2021. |
Optimising long-term outcomes using real-world fluent objectives: an application to football Inproceedings 20th International Conference on Autonomous Agents and Multiagent Systems (03/05/21 - 07/05/21), pp. 196–204, 2021. |
What happened next? Using deep learning to value defensive actions in football event-data Inproceedings KDD 2021 (14/08/21 - 18/08/21), pp. 3394–3403, 2021. |
2020 |
Optimising daily fantasy sports teams with artificial intelligence Journal Article International Journal of Computer Science in Sport, 19 (2), 2020. |
A critical comparison of machine learning classifiers to predict match outcomes in the NFL Journal Article International Journal of Computer Science in Sport, 19 (2), 2020. |
Mechanism design for efficient allocation of electric vehicles to charging stations Inproceedings SETN 2020: 11th Hellenic Conference on Artificial Intelligence, pp. 10–15, 2020. |
Solving the fair electric load shedding problem in developing countries Journal Article Autonomous Agents and Multi-Agent Systems, 34 (1), pp. 12, 2020. |
Solving the fair electric load shedding problem in developing countries Journal Article Auton. Agents Multi Agent Syst., 34 (1), pp. 12, 2020. |
An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations Journal Article Simul. Model. Pract. Theory, 100 , pp. 102040, 2020. |
Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer Journal Article IEEE Trans. Intell. Transp. Syst., 21 (5), pp. 2128–2138, 2020. |
Learning the Value of Teamwork to Form Efficient Teams Inproceedings The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 7063–7070, AAAI Press, 2020. |
ODSS: Efficient Hybridization for Optimal Coalition Structure Generation Inproceedings The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 7079–7086, AAAI Press, 2020. |
Optimising Game Tactics for Football Inproceedings Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 141–149, International Foundation for Autonomous Agents and Multiagent Systems, 2020. |
Solving the Fair Electric Load Shedding Problem in Developing Countries Inproceedings Seghrouchni, Amal El Fallah; Sukthankar, Gita; An, Bo; -, Neil Yorke (Ed.): Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020, pp. 2155–2157, International Foundation for Autonomous Agents and Multiagent Systems, 2020. |
Monte-Carlo Tree Search for Scalable Coalition Formation Inproceedings Bessiere, Christian (Ed.): Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 407–413, ijcai.org, 2020. |
Mechanism design for efficient allocation of electric vehicles to charging stations Inproceedings Spyropoulos, Constantine D; Varlamis, Iraklis; Androutsopoulos, Ion; Malakasiotis, Prodromos (Ed.): SETN 2020: 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, September 2-4, 2020, pp. 10–15, ACM, 2020. |
Optimising Game Tactics for Football Journal Article CoRR, abs/2003.10294 , 2020. |
Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints Journal Article CoRR, abs/2003.13806 , 2020. |
Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations Journal Article CoRR, abs/2007.09715 , 2020. |
2019 |
Model Checking Human-Agent Collectives for Responsible AI Inproceedings 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1–8, IEEE 2019. |
Artificial intelligence for team sports: a survey Journal Article The Knowledge Engineering Review, 34 , 2019. |
Multimodal human aerobotic interaction Incollection Unmanned Aerial Vehicles: Breakthroughs in Research and Practice, pp. 142–165, IGI Global, 2019. |
Tracking the Consumption of Home Essentials Inproceedings Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 639, ACM 2019. |
Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer Journal Article IEEE Transactions on Intelligent Transportation Systems, 2019. |
An Agent-based Negotiation Scheme for the Distribution of Electric Vehicles Across a Set of Charging Stations Journal Article Simulation Modelling Practice and Theory, pp. 102040, 2019. |
Effects of Varying Noise Levels and Lighting Levels on Multimodal Speech and Visual Gesture Interaction with Aerobots Journal Article Applied Sciences, 9 (10), pp. 2066, 2019. |
2018 |
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article Artificial Intelligence, 262 , pp. 248–278, 2018. |
The multimodal speech and visual gesture (mSVG) control model for a practical patrol, search, and rescue aerobot Inproceedings 19th Towards Autonomous Robotic Systems (TAROS) Conference 2018, pp. 423–437, Springer, 2018. |
Automated negotiation for opportunistic energy trading between neighbouring wireless sensor networks Inproceedings 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (31/10/18), 2018. |
IEEE International Conference on Applied System Innovation (IEEE ICASI) 2018, pp. 842–845, IEEE, 2018. |
Seeing (movement) is believing: the effect of motion on perception of automatic systems performance Journal Article Human-Computer Interaction, pp. 1-51, 2018. |
On the distinctiveness of the electricity load profile Journal Article Pattern Recognition, 74 (Supplement C), pp. 317–325, 2018, ISSN: 0031-3203. |
Algorithms for fair load shedding in developing countries Inproceedings Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1590-1596, 2018. |
Algorithms to manage load shedding events in developing countries Inproceedings Proceedings of the International Conference on Autonomous and Multi-Agent Systems (AAMAS), pp. 2034-2036, 2018. |
Learning from the veg box: Designing unpredictability in agency delegation Inproceedings CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018. |
