@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}
}
In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.
http://eprints.soton.ac.uk/350677/
@inproceedings{eps350677,
title = {Collabmap: crowdsourcing maps for emergency planning},
author = {Sarvapali D. Ramchurn and Trung Dong Huynh and Matteo Venanzi and Bing Shi},
url = {http://eprints.soton.ac.uk/350677/},
year = {2013},
date = {2013-01-01},
booktitle = {The 5th Annual ACM Web Science Conference},
pages = {326--335},
abstract = {In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of managing Electric Vehicle (EV) charging at charging points in a city to ensure that the load on the charging points remains within the desired limits while minimizing the inconvenience to EV owners. We develop solutions that treat charging points and EV users as self-interested agents that aim to maximize their profit and minimize the impact on their schedule. In particular, we propose variants of a decentralised and dynamic approach as well as an optimal centralised static approach. We evaluated these solutions in a real setting based on the road network and the location of parking garages of a UK city and show that the optimal centralised (non-dynamic) solution manages the congestion the best but does not scale well, while the decentralised solutions scale to thousands of agents.
http://eprints.soton.ac.uk/356081/
@inproceedings{eps356081,
title = {Congestion management for urban EV charging systems},
author = {Emmanouil Rigas and Sarvapali Ramchurn and Nick Bassiliades and Georgios Koutitas},
url = {http://eprints.soton.ac.uk/356081/},
year = {2013},
date = {2013-01-01},
booktitle = {4th IEEE International Conference on Smart Grid Communications (SmartGridComm)},
volume = {4},
publisher = {IEEE},
abstract = {We consider the problem of managing Electric Vehicle (EV) charging at charging points in a city to ensure that the load on the charging points remains within the desired limits while minimizing the inconvenience to EV owners. We develop solutions that treat charging points and EV users as self-interested agents that aim to maximize their profit and minimize the impact on their schedule. In particular, we propose variants of a decentralised and dynamic approach as well as an optimal centralised static approach. We evaluated these solutions in a real setting based on the road network and the location of parking garages of a UK city and show that the optimal centralised (non-dynamic) solution manages the congestion the best but does not scale well, while the decentralised solutions scale to thousands of agents.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We develop the first parallel algorithm for Coalition Structure Generation (CSG), which is central to many multi-agent systems applications. Our approach involves distributing the key steps of a dynamic programming approach to CSG across computational nodes on a Graphics Processing Unit (GPU) such that each of the thousands of threads of computation can be used to perform small computations that speed up the overall process. In so doing, we solve important challenges that arise in solving combinatorial optimisation problems on GPUs such as the efficient allocation of memory and computational threads to every step of the algorithm. In our empirical evaluations on a standard GPU, our results show an improvement of orders of magnitude over current dynamic programming approaches with an ever increasing divergence between the CPU and GPU-based algorithms in terms of growth. Thus, our algorithm is able to solve the CSG problem for 29 agents in one hour and thirty minutes as opposed to three days for the current state of the art dynamic programming algorithms.
http://eprints.soton.ac.uk/352204/
@inproceedings{eps352204,
title = {Solving the coalition structure generation problem on a GPU},
author = {Kim Svensson and Sarvapali Ramchurn and Francisco Cruz and Juan-Antonio Rodriguez-Aguilar and Jesus Cerquides},
url = {http://eprints.soton.ac.uk/352204/},
year = {2013},
date = {2013-01-01},
booktitle = {6th International Workshop on Optimisation in Multi-Agent Systems},
abstract = {We develop the first parallel algorithm for Coalition Structure Generation (CSG), which is central to many multi-agent systems applications. Our approach involves distributing the key steps of a dynamic programming approach to CSG across computational nodes on a Graphics Processing Unit (GPU) such that each of the thousands of threads of computation can be used to perform small computations that speed up the overall process. In so doing, we solve important challenges that arise in solving combinatorial optimisation problems on GPUs such as the efficient allocation of memory and computational threads to every step of the algorithm. In our empirical evaluations on a standard GPU, our results show an improvement of orders of magnitude over current dynamic programming approaches with an ever increasing divergence between the CPU and GPU-based algorithms in terms of growth. Thus, our algorithm is able to solve the CSG problem for 29 agents in one hour and thirty minutes as opposed to three days for the current state of the art dynamic programming algorithms.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{eps351242,
title = {Forecasting multi-appliance usage for smart home energy management},
author = {Ngoc Cuong Truong and James McInerney and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351242/},
year = {2013},
date = {2013-01-01},
booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{eps351238,
title = {Activity prediction for agent-based home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and D. Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/351238/},
year = {2013},
date = {2013-01-01},
booktitle = {Agent Technologies for Energy Systems (ATES 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{eps351240,
title = {Towards appliance usage prediction 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/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
http://eprints.soton.ac.uk/338804/
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216--225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
http://eprints.soton.ac.uk/340068/
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168--182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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