@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}
}
@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}
}
@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}
}
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}
}
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
http://eprints.soton.ac.uk/340382/
@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}
}
Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.
http://eprints.soton.ac.uk/273142/
@article{eps273142,
title = {Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid},
author = {Sam Miller and Sarvapali D Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/273142/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
journal = {In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
abstract = {Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
http://eprints.soton.ac.uk/272606/
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86--97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.
http://eprints.soton.ac.uk/344492/
@inproceedings{eps344492,
title = {Practical distributed coalition formation via heuristic negotiation in social networks},
author = {Sarvapali D. Ramchurn and Enrico Gerding and N.R. Jennings and Jun Hu},
url = {http://eprints.soton.ac.uk/344492/},
year = {2012},
date = {2012-01-01},
booktitle = {Fifth International Workshop on Optimisation in Multi-Agent Systems (OPTMAS)},
abstract = {We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
http://eprints.soton.ac.uk/349083/
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613--614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators -- commonly termed the ?smart grid? -- represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
http://eprints.soton.ac.uk/337560/
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166--2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators -- commonly termed the ?smart grid? -- represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{eps339215,
title = {Predicting energy consumption activities for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/339215/},
year = {2012},
date = {2012-01-01},
booktitle = {Agent Technologies for Energy Systems (ATES 2012)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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