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2018
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article
In: Artificial Intelligence, vol. 262, pp. 248–278, 2018.
Abstract | Links | BibTeX | Tags: Electric Vehicles, Heuristics, Mobility on Demand, optimisation, Scheduling
@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 = {Electric Vehicles, Heuristics, Mobility on Demand, optimisation, Scheduling},
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.
2015
Bistaffa, Sarvapali D. Ramchurn Alessandro Farinelli Filippo
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
Abstract | Links | BibTeX | Tags: Coalition Formation, optimisation, Ridesharing, Social Networks
@inproceedings{bistaffa:etal:2015,
title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},
author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},
url = {http://eprints.soton.ac.uk/372048/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},
keywords = {Coalition Formation, optimisation, Ridesharing, Social Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
Links | BibTeX | Tags: Energy, optimisation, smart grid
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},
url = {http://doi.acm.org/10.1145/2768510.2768531},
isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
pages = {7–16},
publisher = {ACM},
address = {Bangalore, India},
series = {e-Energy '15},
keywords = {Energy, optimisation, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
Abstract | Links | BibTeX | Tags: electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
Abstract | Links | BibTeX | Tags: multi-agent systems, optimisation, sequential decision making, team formation
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {multi-agent systems, optimisation, sequential decision making, team formation},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
2009
Ramchurn, Sarvapali D.; Mezzetti, Claudio; Giovannucci, Andrea; Rodriguez, Juan A.; Dash, Rajdeep K.; Jennings, Nicholas R.
Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty Journal Article
In: Journal of Artificial Intelligence Research, vol. 35, pp. 1–41, 2009.
Abstract | Links | BibTeX | Tags: mechanism design, optimisation, Trust, uncertainty
@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1–41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {mechanism design, optimisation, Trust, uncertainty},
pubstate = {published},
tppubtype = {article}
}
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).
1.
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article
In: Artificial Intelligence, vol. 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.
2.
Bistaffa, Sarvapali D. Ramchurn Alessandro Farinelli Filippo
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{bistaffa:etal:2015,
title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},
author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},
url = {http://eprints.soton.ac.uk/372048/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
3.
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},
url = {http://doi.acm.org/10.1145/2768510.2768531},
isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
pages = {7–16},
publisher = {ACM},
address = {Bangalore, India},
series = {e-Energy '15},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
5.
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
6.
Ramchurn, Sarvapali D.; Mezzetti, Claudio; Giovannucci, Andrea; Rodriguez, Juan A.; Dash, Rajdeep K.; Jennings, Nicholas R.
Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty Journal Article
In: Journal of Artificial Intelligence Research, vol. 35, pp. 1–41, 2009.
@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1–41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
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Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).
2018
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article
In: Artificial Intelligence, vol. 262, pp. 248–278, 2018.
Abstract | Links | BibTeX | Tags: Electric Vehicles, Heuristics, Mobility on Demand, optimisation, Scheduling
@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 = {Electric Vehicles, Heuristics, Mobility on Demand, optimisation, Scheduling},
pubstate = {published},
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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.
2015
Bistaffa, Sarvapali D. Ramchurn Alessandro Farinelli Filippo
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
Abstract | Links | BibTeX | Tags: Coalition Formation, optimisation, Ridesharing, Social Networks
@inproceedings{bistaffa:etal:2015,
title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},
author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},
url = {http://eprints.soton.ac.uk/372048/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},
keywords = {Coalition Formation, optimisation, Ridesharing, Social Networks},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
Links | BibTeX | Tags: Energy, optimisation, smart grid
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},
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isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
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series = {e-Energy '15},
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2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
Abstract | Links | BibTeX | Tags: electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {electricity, Energy, group buying, optimisation, provenance, recommender systems, smart grid},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
Abstract | Links | BibTeX | Tags: multi-agent systems, optimisation, sequential decision making, team formation
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {multi-agent systems, optimisation, sequential decision making, team formation},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
2009
Ramchurn, Sarvapali D.; Mezzetti, Claudio; Giovannucci, Andrea; Rodriguez, Juan A.; Dash, Rajdeep K.; Jennings, Nicholas R.
Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty Journal Article
In: Journal of Artificial Intelligence Research, vol. 35, pp. 1–41, 2009.
Abstract | Links | BibTeX | Tags: mechanism design, optimisation, Trust, uncertainty
@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1–41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {mechanism design, optimisation, Trust, uncertainty},
pubstate = {published},
tppubtype = {article}
}
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).
1.
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article
In: Artificial Intelligence, vol. 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},
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}
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.
2.
Bistaffa, Sarvapali D. Ramchurn Alessandro Farinelli Filippo
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{bistaffa:etal:2015,
title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},
author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},
url = {http://eprints.soton.ac.uk/372048/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
3.
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},
url = {http://doi.acm.org/10.1145/2768510.2768531},
isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
pages = {7–16},
publisher = {ACM},
address = {Bangalore, India},
series = {e-Energy '15},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
4.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
5.
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
6.
Ramchurn, Sarvapali D.; Mezzetti, Claudio; Giovannucci, Andrea; Rodriguez, Juan A.; Dash, Rajdeep K.; Jennings, Nicholas R.
Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty Journal Article
In: Journal of Artificial Intelligence Research, vol. 35, pp. 1–41, 2009.
@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1–41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick
Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes Journal Article
In: Artificial Intelligence, vol. 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.
2015
Bistaffa, Sarvapali D. Ramchurn Alessandro Farinelli Filippo
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{bistaffa:etal:2015,
title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing},
author = {Sarvapali D. Ramchurn Alessandro Farinelli Filippo Bistaffa},
url = {http://eprints.soton.ac.uk/372048/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, ar- range one-time rides at a very short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system mod- elling such problem as a graph constrained coalition forma- tion (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the ap- plicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to −36.22%). More- over, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).
Holyhead, James C.; Ramchurn, Sarvapali D.; Rogers, Alex
Consumer Targeting in Residential Demand Response Programmes Proceedings Article
In: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, pp. 7–16, ACM, Bangalore, India, 2015, ISBN: 978-1-4503-3609-3.
@inproceedings{Holyhead:2015:CTR:2768510.2768531,
title = {Consumer Targeting in Residential Demand Response Programmes},
author = {James C. Holyhead and Sarvapali D. Ramchurn and Alex Rogers},
url = {http://doi.acm.org/10.1145/2768510.2768531},
isbn = {978-1-4503-3609-3},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems},
pages = {7–16},
publisher = {ACM},
address = {Bangalore, India},
series = {e-Energy '15},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
2012
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
2009
Ramchurn, Sarvapali D.; Mezzetti, Claudio; Giovannucci, Andrea; Rodriguez, Juan A.; Dash, Rajdeep K.; Jennings, Nicholas R.
Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty Journal Article
In: Journal of Artificial Intelligence Research, vol. 35, pp. 1–41, 2009.
@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1–41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).