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101.
Calliere, Romain; Aknine, Samir; Nongaillard, Antoine; Ramchurn, Sarvapali
Managing energy markets in future smart grids using bilateral contracts Proceedings Article
In: European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016.
@inproceedings{cailliere:hal-01329606,
title = {Managing energy markets in future smart grids using bilateral contracts},
author = {Romain Calliere and Samir Aknine and Antoine Nongaillard and Sarvapali Ramchurn},
url = {https://hal.archives-ouvertes.fr/hal-01329606},
year = {2016},
date = {2016-01-01},
booktitle = {European Conference on Artificial Intelligence (ECAI)},
address = {The Hague, Netherlands},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
102.
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article
In: IEEE Trans. Cybernetics, vol. 46, no. 12, pp. 3364–3376, 2016.
@article{DBLP:journals/tcyb/ChenWSCR16,
title = {Decentralized Patrolling Under Constraints in Dynamic Environments},
author = {Shaofei Chen and Feng Wu and Lincheng Shen and Jing Chen and Sarvapali D Ramchurn},
url = {https://doi.org/10.1109/TCYB.2015.2505737},
doi = {10.1109/TCYB.2015.2505737},
year = {2016},
date = {2016-01-01},
journal = {IEEE Trans. Cybernetics},
volume = {46},
number = {12},
pages = {3364–3376},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
103.
-, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D
Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Proceedings Article
In: 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016, pp. 302–307, 2016.
@inproceedings{DBLP:conf/smartgridcomm/KoufakisRBR16,
title = {Towards an optimal EV charging scheduling scheme with V2G and
V2V energy transfer},
author = {Alexandros - and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn},
url = {https://doi.org/10.1109/SmartGridComm.2016.7778778},
doi = {10.1109/SmartGridComm.2016.7778778},
year = {2016},
date = {2016-01-01},
booktitle = {2016 IEEE International Conference on Smart Grid Communications,
SmartGridComm 2016, Sydney, Australia, November 6-9, 2016},
pages = {302–307},
crossref = {DBLP:conf/smartgridcomm/2016},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
104.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
CrowdAR: augmenting live video with a real-time crowd Proceedings Article
In: HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing, 2015.
@inproceedings{eps382948,
title = {CrowdAR: augmenting live video with a real-time crowd},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/382948/},
year = {2015},
date = {2015-11-01},
booktitle = {HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing},
abstract = {Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.
105.
Bistaffa, Alessandro Farinelli Georgios Chalkiadakis Filippo; Ramchurn, Sarvapali D.
Recommending Fair Payments for Large-Scale Social Ridesharing Proceedings Article
In: ACM Conference on Recommender Systems (Recsys), 2015.
@inproceedings{bistaffaetal2015,
title = {Recommending Fair Payments for Large-Scale Social Ridesharing},
author = {Alessandro Farinelli Georgios Chalkiadakis Filippo Bistaffa and Sarvapali D. Ramchurn},
url = {https://www.sramchurn.com/wp-content/uploads/2017/02/2015recsys.pdf},
year = {2015},
date = {2015-09-16},
booktitle = {ACM Conference on Recommender Systems (Recsys)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
106.
Alan, Alper T.; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N. R.
Managing energy tariffs with agents: a field study of a future smart energy system at home Proceedings Article
In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015.
@inproceedings{eps378696,
title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},
author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/378696/},
year = {2015},
date = {2015-07-01},
booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
107.
Alan, Alper T.; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N. R.
Managing energy tariffs with agents: a field study of a future smart energy system at home Proceedings Article
In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp), 2015.
@inproceedings{eps378696b,
title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},
author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/378696/},
year = {2015},
date = {2015-07-01},
booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
108.
Fischer, Tom Rodden Stuart Reeves Joel E.; Jones, David
Building a Bird's Eye View: Collaborative Work Proceedings Article
In: Proceedings of SIGCHI (To appear), 2015.
@inproceedings{fischer:etal:2015,
title = {Building a Bird's Eye View: Collaborative Work},
author = {Tom Rodden Stuart Reeves Joel E. Fischer and David Jones},
url = {https://www.sramchurn.com/wp-content/uploads/2015/01/pn1018-fischerA.pdf},
year = {2015},
date = {2015-05-01},
booktitle = {Proceedings of SIGCHI (To appear)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
109.
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
110.
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).
111.
Zhao, Enrico H. Gerding Sarvapali D. Ramchurn Dengji; Jennings, Nicholas R.
Balanced Trade Reduction for Dual-Role Exchange Markets Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{zhao:etal:2015,
title = {Balanced Trade Reduction for Dual-Role Exchange Markets},
author = {Enrico H. Gerding Sarvapali D. Ramchurn Dengji Zhao and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/372050/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.
112.
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
113.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
114.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
115.
Ramchurn, Sarvapali; Wu, Feng; Fischer, Joel; Reece, Steven; Jiang, Wenchao; Roberts, Stephen J.; Rodden, Tom; Jennings, Nicholas R.
Human-agent collaboration for disaster response Journal Article
In: Journal of Autonomous Agents and Multi-Agent Systems, pp. 1–30, 2015.
@article{eps374063,
title = {Human-agent collaboration for disaster response},
author = {Sarvapali Ramchurn and Feng Wu and Joel Fischer and Steven Reece and Wenchao Jiang and Stephen J. Roberts and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/374063/},
year = {2015},
date = {2015-01-01},
journal = {Journal of Autonomous Agents and Multi-Agent Systems},
pages = {1–30},
publisher = {Springer},
abstract = {In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.
116.
Alam, Muddasser; Gerding, Enrico H.; Rogers, Alex; Ramchurn, Sarvapali D.
A scalable, decentralised multi-issue negotiation protocol for energy exchange Proceedings Article
In: International Joint Conference on Artificial Intelligence (IJCAI), 2015.
@inproceedings{eps376618,
title = {A scalable, decentralised multi-issue negotiation protocol for energy exchange},
author = {Muddasser Alam and Enrico H. Gerding and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/376618/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.
117.
Wu, Feng; Ramchurn, Sarvapali; Jiang, Wenchao; Fischer, Joel; Rodden, Tom; Jennings, Nicholas R.
Agile Planning for Real-World Disaster Response Proceedings Article
In: International Joint Conference on Artificial Intelligence, 2015.
@inproceedings{eps377186,
title = {Agile Planning for Real-World Disaster Response},
author = {Feng Wu and Sarvapali Ramchurn and Wenchao Jiang and Joel Fischer and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/377186/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence},
abstract = {We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.
118.
Ramchurn, Sarvapali; Fischer, Joel; Ikuno, Yuki; Wu, Feng; Flann, Jack; Waldock, Antony
A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments Proceedings Article
In: International Joint Conference on Artificial Intelligence, 2015.
@inproceedings{eps377185,
title = {A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments},
author = {Sarvapali Ramchurn and Joel Fischer and Yuki Ikuno and Feng Wu and Jack Flann and Antony Waldock},
url = {http://eprints.soton.ac.uk/377185/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence},
abstract = {We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.},
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We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.
119.
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}
}
120.
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D
Multi-Agent Patrolling under Uncertainty and Threats Journal Article
In: PLoS ONE, vol. 10, no. 6, pp. e0130154, 2015, ISBN: 1932-6203.
@article{chen:etal:2016,
title = {Multi-Agent Patrolling under Uncertainty and Threats},
author = {Shaofei Chen and Feng Wu and Lincheng Shen and Jing Chen and Sarvapali D Ramchurn},
editor = {Yong Deng},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472811/},
doi = {10.1371/journal.pone.0130154},
isbn = {1932-6203},
year = {2015},
date = {2015-01-01},
journal = {PLoS ONE},
volume = {10},
number = {6},
pages = {e0130154},
publisher = {Public Library of Science},
abstract = {We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.
Sorry, no publications matched your criteria.
101.
Calliere, Romain; Aknine, Samir; Nongaillard, Antoine; Ramchurn, Sarvapali
Managing energy markets in future smart grids using bilateral contracts Proceedings Article
In: European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016.
@inproceedings{cailliere:hal-01329606,
title = {Managing energy markets in future smart grids using bilateral contracts},
author = {Romain Calliere and Samir Aknine and Antoine Nongaillard and Sarvapali Ramchurn},
url = {https://hal.archives-ouvertes.fr/hal-01329606},
year = {2016},
date = {2016-01-01},
booktitle = {European Conference on Artificial Intelligence (ECAI)},
address = {The Hague, Netherlands},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
102.
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article
In: IEEE Trans. Cybernetics, vol. 46, no. 12, pp. 3364–3376, 2016.
@article{DBLP:journals/tcyb/ChenWSCR16,
title = {Decentralized Patrolling Under Constraints in Dynamic Environments},
author = {Shaofei Chen and Feng Wu and Lincheng Shen and Jing Chen and Sarvapali D Ramchurn},
url = {https://doi.org/10.1109/TCYB.2015.2505737},
doi = {10.1109/TCYB.2015.2505737},
year = {2016},
date = {2016-01-01},
journal = {IEEE Trans. Cybernetics},
volume = {46},
number = {12},
pages = {3364–3376},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
103.
-, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D
Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Proceedings Article
In: 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016, pp. 302–307, 2016.
@inproceedings{DBLP:conf/smartgridcomm/KoufakisRBR16,
title = {Towards an optimal EV charging scheduling scheme with V2G and
V2V energy transfer},
author = {Alexandros - and Emmanouil S Rigas and Nick Bassiliades and Sarvapali D Ramchurn},
url = {https://doi.org/10.1109/SmartGridComm.2016.7778778},
doi = {10.1109/SmartGridComm.2016.7778778},
year = {2016},
date = {2016-01-01},
booktitle = {2016 IEEE International Conference on Smart Grid Communications,
SmartGridComm 2016, Sydney, Australia, November 6-9, 2016},
pages = {302–307},
crossref = {DBLP:conf/smartgridcomm/2016},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
104.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
CrowdAR: augmenting live video with a real-time crowd Proceedings Article
In: HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing, 2015.
@inproceedings{eps382948,
title = {CrowdAR: augmenting live video with a real-time crowd},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/382948/},
year = {2015},
date = {2015-11-01},
booktitle = {HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing},
abstract = {Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.
105.
Bistaffa, Alessandro Farinelli Georgios Chalkiadakis Filippo; Ramchurn, Sarvapali D.
Recommending Fair Payments for Large-Scale Social Ridesharing Proceedings Article
In: ACM Conference on Recommender Systems (Recsys), 2015.
@inproceedings{bistaffaetal2015,
title = {Recommending Fair Payments for Large-Scale Social Ridesharing},
author = {Alessandro Farinelli Georgios Chalkiadakis Filippo Bistaffa and Sarvapali D. Ramchurn},
url = {https://www.sramchurn.com/wp-content/uploads/2017/02/2015recsys.pdf},
year = {2015},
date = {2015-09-16},
booktitle = {ACM Conference on Recommender Systems (Recsys)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
106.
Alan, Alper T.; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N. R.
Managing energy tariffs with agents: a field study of a future smart energy system at home Proceedings Article
In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015.
@inproceedings{eps378696,
title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},
author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/378696/},
year = {2015},
date = {2015-07-01},
booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
107.
Alan, Alper T.; Costanza, Enrico; Ramchurn, Sarvapali; Fischer, Joel; Rodden, Tom; Jennings, N. R.
Managing energy tariffs with agents: a field study of a future smart energy system at home Proceedings Article
In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp), 2015.
@inproceedings{eps378696b,
title = {Managing energy tariffs with agents: a field study of a future smart energy system at home},
author = {Alper T. Alan and Enrico Costanza and Sarvapali Ramchurn and Joel Fischer and Tom Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/378696/},
year = {2015},
date = {2015-07-01},
booktitle = {Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (Ubicomp)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
108.
Fischer, Tom Rodden Stuart Reeves Joel E.; Jones, David
Building a Bird's Eye View: Collaborative Work Proceedings Article
In: Proceedings of SIGCHI (To appear), 2015.
@inproceedings{fischer:etal:2015,
title = {Building a Bird's Eye View: Collaborative Work},
author = {Tom Rodden Stuart Reeves Joel E. Fischer and David Jones},
url = {https://www.sramchurn.com/wp-content/uploads/2015/01/pn1018-fischerA.pdf},
year = {2015},
date = {2015-05-01},
booktitle = {Proceedings of SIGCHI (To appear)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
109.
Tran-Thanh, Avi Rosenfeld Trung Dong Huynh Long
Crowdsourcing Complex Workflows under Budget Constraints Proceedings Article
In: Proceedings of the AAAI Conference, AAAI, 2015.
@inproceedings{tranh:Etal:2015,
title = {Crowdsourcing Complex Workflows under Budget Constraints},
author = {Avi Rosenfeld Trung Dong Huynh Long Tran-Thanh},
url = {http://eprints.soton.ac.uk/372107/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
publisher = {AAAI},
abstract = {We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider the problem of task allocation in crowdsourc- ing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a bud- get constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then de- termines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the cor- responding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
110.
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).
111.
Zhao, Enrico H. Gerding Sarvapali D. Ramchurn Dengji; Jennings, Nicholas R.
Balanced Trade Reduction for Dual-Role Exchange Markets Proceedings Article
In: Proceedings of the AAAI Conference, 2015.
@inproceedings{zhao:etal:2015,
title = {Balanced Trade Reduction for Dual-Role Exchange Markets},
author = {Enrico H. Gerding Sarvapali D. Ramchurn Dengji Zhao and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/372050/},
year = {2015},
date = {2015-01-25},
booktitle = {Proceedings of the AAAI Conference},
abstract = {We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee’s trade reduc- tion approach, we propose a new trade reduction mech- anism, called balanced trade reduction, that is incen- tive compatible and also provides flexible trade-offs be- tween efficiency and deficit.
112.
Rigas, Nick Bassiliades Sarvapali D. Ramchurn Emmanouil
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article
In: IEEE Transactions on Intelligent Transportation Systems, 2015.
@article{rigas:etal:2015,
title = {Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey},
author = {Nick Bassiliades Sarvapali D. Ramchurn Emmanouil Rigas},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7000557&filter%3DAND%28p_IS_Number%3A7174612%29},
year = {2015},
date = {2015-01-16},
journal = {IEEE Transactions on Intelligent Transportation Systems},
abstract = {Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Along with the development of Smart Grids, the wide adoption of Electric Vehicles (EVs) is seen as a catalyst to the reduction of CO2 emissions and more intelligent transportation systems. In particular, EVs augment the grid with the ability to store energy at some points in the network and give it back at others and therefore help optimise the use of energy from intermittent renewable energy sources and let users refill their cars in a variety of locations. However, a number of challenges need to be addressed if such benefits are to be achieved. On the one hand, given their limited range and costs involved in charging EV batteries, it is important to design algorithms that will minimise costs while avoid users being stranded. On the other hand, collectives of EVs need to be organized in such a way as to avoid peaks on the grid that may result in high electricity prices and overload local distribution grids. In order to meet such challenges, a number of technological solutions have been proposed. In this paper, we focus on those that utilise artificial intelligence techniques to render EVs and the systems that manage collectives of EVs smarter. In particular, we provide a survey of the literature and identify the commonalities and key differences in the approaches. This allows us to develop a classification of key techniques and benchmarks that can be used to advance the state-of-the art in this space.
113.
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali
Real-time opinion aggregation methods for crowd robotics Proceedings Article
In: Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015.
@inproceedings{eps375287,
title = {Real-time opinion aggregation methods for crowd robotics},
author = {Elliot Salisbury and Sebastian Stein and Sarvapali Ramchurn},
url = {http://eprints.soton.ac.uk/375287/},
year = {2015},
date = {2015-01-01},
booktitle = {Autonomous Agents and Multiagent Systems (AAMAS 2015)},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenarios
114.
Ramchurn, Sarvapali; Simpson, Edwin; Fischer, Joel; Huynh, Trung Dong; Ikuno, Yuki; Reece, Steven; Jiang, Wenchao; Wu, Feng; Flann, Jack; Roberts, S. J.; Moreau, Luc; Rodden, T.; Jennings, N. R.
HAC-ER: A disaster response system based on human-agent collectives Proceedings Article
In: 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015.
@inproceedings{eps374070,
title = {HAC-ER: A disaster response system based on human-agent collectives},
author = {Sarvapali Ramchurn and Edwin Simpson and Joel Fischer and Trung Dong Huynh and Yuki Ikuno and Steven Reece and Wenchao Jiang and Feng Wu and Jack Flann and S. J. Roberts and Luc Moreau and T. Rodden and N. R. Jennings},
url = {http://eprints.soton.ac.uk/374070/},
year = {2015},
date = {2015-01-01},
booktitle = {14th International Conference on Autonomous Agents and Multi-Agent Systems},
abstract = {This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emer- gency responders by enabling humans and agents, using state-of- the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC- ER utilises crowdsourcing combined with machine learning to ex- tract situational awareness information from large streams of re- ports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a pro- totype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations.
115.
Ramchurn, Sarvapali; Wu, Feng; Fischer, Joel; Reece, Steven; Jiang, Wenchao; Roberts, Stephen J.; Rodden, Tom; Jennings, Nicholas R.
Human-agent collaboration for disaster response Journal Article
In: Journal of Autonomous Agents and Multi-Agent Systems, pp. 1–30, 2015.
@article{eps374063,
title = {Human-agent collaboration for disaster response},
author = {Sarvapali Ramchurn and Feng Wu and Joel Fischer and Steven Reece and Wenchao Jiang and Stephen J. Roberts and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/374063/},
year = {2015},
date = {2015-01-01},
journal = {Journal of Autonomous Agents and Multi-Agent Systems},
pages = {1–30},
publisher = {Springer},
abstract = {In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.},
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In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a Multi-Agent Markov Decision Process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.
116.
Alam, Muddasser; Gerding, Enrico H.; Rogers, Alex; Ramchurn, Sarvapali D.
A scalable, decentralised multi-issue negotiation protocol for energy exchange Proceedings Article
In: International Joint Conference on Artificial Intelligence (IJCAI), 2015.
@inproceedings{eps376618,
title = {A scalable, decentralised multi-issue negotiation protocol for energy exchange},
author = {Muddasser Alam and Enrico H. Gerding and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/376618/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
abstract = {We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our protocol is concurrent, scalable and; under certain conditions; leads to Pareto-optimal outcomes.
117.
Wu, Feng; Ramchurn, Sarvapali; Jiang, Wenchao; Fischer, Joel; Rodden, Tom; Jennings, Nicholas R.
Agile Planning for Real-World Disaster Response Proceedings Article
In: International Joint Conference on Artificial Intelligence, 2015.
@inproceedings{eps377186,
title = {Agile Planning for Real-World Disaster Response},
author = {Feng Wu and Sarvapali Ramchurn and Wenchao Jiang and Joel Fischer and Tom Rodden and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/377186/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence},
abstract = {We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a setting where an agent-based planner instructs teams of human emergency responders to perform tasks in the real world. Due to uncertainty in the environment and the inability of the planner to consider all human preferences and all attributes of the real-world, humans may reject plans computed by the agent. A na??ve solution that replans given a rejection is inefficient and does not guarantee the new plan will be acceptable. Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model. We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans.
118.
Ramchurn, Sarvapali; Fischer, Joel; Ikuno, Yuki; Wu, Feng; Flann, Jack; Waldock, Antony
A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments Proceedings Article
In: International Joint Conference on Artificial Intelligence, 2015.
@inproceedings{eps377185,
title = {A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments},
author = {Sarvapali Ramchurn and Joel Fischer and Yuki Ikuno and Feng Wu and Jack Flann and Antony Waldock},
url = {http://eprints.soton.ac.uk/377185/},
year = {2015},
date = {2015-01-01},
booktitle = {International Joint Conference on Artificial Intelligence},
abstract = {We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.
119.
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}
}
120.
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D
Multi-Agent Patrolling under Uncertainty and Threats Journal Article
In: PLoS ONE, vol. 10, no. 6, pp. e0130154, 2015, ISBN: 1932-6203.
@article{chen:etal:2016,
title = {Multi-Agent Patrolling under Uncertainty and Threats},
author = {Shaofei Chen and Feng Wu and Lincheng Shen and Jing Chen and Sarvapali D Ramchurn},
editor = {Yong Deng},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472811/},
doi = {10.1371/journal.pone.0130154},
isbn = {1932-6203},
year = {2015},
date = {2015-01-01},
journal = {PLoS ONE},
volume = {10},
number = {6},
pages = {e0130154},
publisher = {Public Library of Science},
abstract = {We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.
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}
}
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