2016 |
Alan, Alper Turan; Shann, Mike; Costanza, Enrico; Ramchurn, Sarvapali; Seuken, Sven It is too hot: an in-situ study of three designs for heating Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. @inproceedings{eps385045b, title = {It is too hot: an in-situ study of three designs for heating}, author = {Alper Turan Alan and Mike Shann and Enrico Costanza and Sarvapali Ramchurn and Sven Seuken}, url = {http://eprints.soton.ac.uk/385045/}, year = {2016}, date = {2016-01-01}, booktitle = {The SIGCHI Conference on Human Factors in Computing Systems}, abstract = {Smart technologies are becoming increasingly ubiquitous, and consequently transforming our lives. Domestic energy use is one of the most talked domain that people may greatly benefit from these technologies. Given this, it is important to understand interactions with smart systems within people?s everyday lives. To this end, we developed and deployed the first heating system that allows its users to control their home heating with real-time prices. In particular, we implemented three different designs of our heating system, and evaluated them with 30 UK households in a four-week in the wild study. Our findings through thematic analysis show that our participants formed different understandings and expectations of the system, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. These findings contribute to our understanding of interactions with smart energy systems and provide key design implications for developing them.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Smart technologies are becoming increasingly ubiquitous, and consequently transforming our lives. Domestic energy use is one of the most talked domain that people may greatly benefit from these technologies. Given this, it is important to understand interactions with smart systems within people?s everyday lives. To this end, we developed and deployed the first heating system that allows its users to control their home heating with real-time prices. In particular, we implemented three different designs of our heating system, and evaluated them with 30 UK households in a four-week in the wild study. Our findings through thematic analysis show that our participants formed different understandings and expectations of the system, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. These findings contribute to our understanding of interactions with smart energy systems and provide key design implications for developing them. |
Bandhyopadhyay, Sambaran; Narayanam, Ramasuri; Kumar, Pratyush; Ramchurn, Sarvapali Dyanand; Arya, Vijay An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid Inproceedings Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI), AAAI Press, 2016. @inproceedings{eps386417, title = {An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid}, author = {Sambaran Bandhyopadhyay and Ramasuri Narayanam and Pratyush Kumar and Sarvapali Dyanand Ramchurn and Vijay Arya}, url = {http://eprints.soton.ac.uk/386417/}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI)}, publisher = {AAAI Press}, abstract = {In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes.We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously.We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes.We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously.We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities. |
Calliere, Romain ; Aknine, Samir ; Nongaillard, Antoine ; Ramchurn, Sarvapali Managing energy markets in future smart grids using bilateral contracts Inproceedings 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 = {Calliere, Romain and Aknine, Samir and Nongaillard, Antoine and Ramchurn, Sarvapali}, 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} } |
Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article IEEE Trans. Cybernetics, 46 (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} } |
-, Alexandros; Rigas, Emmanouil S; Bassiliades, Nick; Ramchurn, Sarvapali D Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Inproceedings 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} } |
2015 |
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali CrowdAR: augmenting live video with a real-time crowd Inproceedings 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. |
Filippo Bistaffa Georgios Chalkiadakis, Alessandro Farinelli ; Ramchurn, Sarvapali D Recommending Fair Payments for Large-Scale Social Ridesharing Inproceedings ACM Conference on Recommender Systems (Recsys), 2015. @inproceedings{bistaffaetal2015, title = {Recommending Fair Payments for Large-Scale Social Ridesharing}, author = {Filippo Bistaffa, Georgios Chalkiadakis, Alessandro Farinelli, and Sarvapali D. Ramchurn}, url = {http://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} } |
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 Inproceedings 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} } |
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 Inproceedings 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} } |
Joel E. Fischer Stuart Reeves, Tom Rodden Steven Reece Sarvapali Ramchurn D; Jones, David Building a Bird's Eye View: Collaborative Work Inproceedings Proceedings of SIGCHI (To appear), 2015. @inproceedings{fischer:etal:2015, title = {Building a Bird\'s Eye View: Collaborative Work }, author = {Joel E. Fischer, Stuart Reeves, Tom Rodden, Steven Reece, Sarvapali D. Ramchurn, and David Jones}, url = {http://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} } |
Long Tran-Thanh Trung Dong Huynh, Avi Rosenfeld Sarvapali Ramchurn Nicholas Jennings D R Crowdsourcing Complex Workflows under Budget Constraints Inproceedings Proceedings of the AAAI Conference, AAAI, 2015. @inproceedings{tranh:Etal:2015, title = {Crowdsourcing Complex Workflows under Budget Constraints}, author = {Long Tran-Thanh, Trung Dong Huynh, Avi Rosenfeld, Sarvapali D. Ramchurn, Nicholas R. Jennings}, 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. |
Filippo Bistaffa Alessandro Farinelli, Sarvapali Ramchurn D Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Inproceedings Proceedings of the AAAI Conference, 2015. @inproceedings{bistaffa:etal:2015, title = {Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing}, author = {Filippo Bistaffa, Alessandro Farinelli, Sarvapali D. Ramchurn}, 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). |
Dengji Zhao Sarvapali D. Ramchurn, Enrico Gerding H; Jennings, Nicholas R Balanced Trade Reduction for Dual-Role Exchange Markets Inproceedings Proceedings of the AAAI Conference, 2015. @inproceedings{zhao:etal:2015, title = {Balanced Trade Reduction for Dual-Role Exchange Markets}, author = {Dengji Zhao, Sarvapali D. Ramchurn, Enrico H. Gerding, 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. |
Emmanouil Rigas Sarvapali D. Ramchurn, Nick Bassiliades Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article 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 = {Emmanouil Rigas, Sarvapali D. Ramchurn, Nick Bassiliades}, 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. |
Salisbury, Elliot; Stein, Sebastian; Ramchurn, Sarvapali Real-time opinion aggregation methods for crowd robotics Inproceedings 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 |
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 Inproceedings 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. |
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 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. |
Alam, Muddasser; Gerding, Enrico H; Rogers, Alex; Ramchurn, Sarvapali D A scalable, decentralised multi-issue negotiation protocol for energy exchange Inproceedings 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. |
Wu, Feng; Ramchurn, Sarvapali; Jiang, Wenchao; Fischer, Joel; Rodden, Tom; Jennings, Nicholas R Agile Planning for Real-World Disaster Response Inproceedings 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. |
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 Inproceedings 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. |
Holyhead, James C; Ramchurn, Sarvapali D; Rogers, Alex Consumer Targeting in Residential Demand Response Programmes Inproceedings 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 = {Holyhead, James C. and Ramchurn, Sarvapali D. and Rogers, Alex}, 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} } |
Chen, Shaofei ; Wu, Feng ; Shen, Lincheng ; Chen, Jing ; Ramchurn, Sarvapali D Multi-Agent Patrolling under Uncertainty and Threats Journal Article PLoS ONE, 10 (6), pp. e0130154, 2015, ISBN: 1932-6203. @article{chen:etal:2016, title = {Multi-Agent Patrolling under Uncertainty and Threats}, author = {Chen, Shaofei and Wu, Feng and Shen, Lincheng and Chen, Jing and Ramchurn, Sarvapali D}, editor = {Deng, Yong}, 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. |
Chen, S; Wu, F; Shen, L; Chen, J; Ramchurn, S D Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article Cybernetics, IEEE Transactions on, PP (99), pp. 1-13, 2015, ISSN: 2168-2267. @article{7362160, title = {Decentralized Patrolling Under Constraints in Dynamic Environments}, author = {Chen, S. and Wu, F. and Shen, L. and Chen, J. and Ramchurn, S.D.}, doi = {10.1109/TCYB.2015.2505737}, issn = {2168-2267}, year = {2015}, date = {2015-01-01}, journal = {Cybernetics, IEEE Transactions on}, volume = {PP}, number = {99}, pages = {1-13}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Kalyanaraman, Shivkumar; Seetharam, Deva P; Shorey, Rajeev; Ramchurn, Sarvapali D; Srivastava, Mani (Ed.) ACM, 2015, ISBN: 978-1-4503-3609-3. @proceedings{DBLP:conf/eenergy/2015, title = {Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, e-Energy 2015, Bangalore, India, July 14-17, 2015}, editor = {Shivkumar Kalyanaraman and Deva P Seetharam and Rajeev Shorey and Sarvapali D Ramchurn and Mani Srivastava}, url = {http://dl.acm.org/citation.cfm?id=2768510}, isbn = {978-1-4503-3609-3}, year = {2015}, date = {2015-01-01}, publisher = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
2014 |
M. Bicego F. Recchia, Farinelli Ramchurn Grosso A S D E Behavioural biometrics using electricity load profiles Journal Article Proceedings of the International Conference on Pattern Recognition, 2014. @article{bicego:etal:2014, title = {Behavioural biometrics using electricity load profiles}, author = {M. Bicego, F. Recchia, A. Farinelli, S. D. Ramchurn, E. Grosso}, url = {http://www.sramchurn.com/wp-content/uploads/2014/10/CR_v1.pdf}, year = {2014}, date = {2014-08-24}, journal = {Proceedings of the International Conference on Pattern Recognition}, abstract = {Modelling behavioural biometric patterns is a key issue for modern user centric applications, aimed at better monitoring users’ activities, understanding their habits and detecting their identity. Following this trend, this paper investigates whether the electrical energy consumption of a user can be a distinctive behavioural biometric trait. In particular we analyse daily and weekly load profiles showing that they are closely related to the identity of the users. Hence, we believe that this level of analysis can open interesting application scenarios in the field of energy management and it provides a good working framework for the continuous development of smart environments with demonstrable benefits on real-world implementations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Modelling behavioural biometric patterns is a key issue for modern user centric applications, aimed at better monitoring users’ activities, understanding their habits and detecting their identity. Following this trend, this paper investigates whether the electrical energy consumption of a user can be a distinctive behavioural biometric trait. In particular we analyse daily and weekly load profiles showing that they are closely related to the identity of the users. Hence, we believe that this level of analysis can open interesting application scenarios in the field of energy management and it provides a good working framework for the continuous development of smart environments with demonstrable benefits on real-world implementations. |
Alan, Alper; Costanza, Enrico; Fischer, J; Ramchurn, Sarvapali; Rodden, T; Jennings, N R A field study of human-agent interaction for electricity tariff switching Inproceedings Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014. @inproceedings{eps360820, title = {A field study of human-agent interaction for electricity tariff switching}, author = {Alper Alan and Enrico Costanza and J. Fischer and Sarvapali Ramchurn and T. Rodden and N.R. Jennings}, url = {http://eprints.soton.ac.uk/360820/}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems}, abstract = {Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called Tariff Agent, to study notions of flexible autonomy in the context of tariff switching. Tariff Agent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonom. |
Costanza, Enrico; Fischer, Joel E; Colley, James A; Rodden, Tom; Ramchurn, Sarvapali; Jennings, Nicholas R Doing the laundry with agents: a field trial of a future smart energy system in the home Inproceedings ACM CHI Conference on Human Factors in Computing Systems 2014, pp. 813–822, ACM 2014. @inproceedings{eps361173, title = {Doing the laundry with agents: a field trial of a future smart energy system in the home}, author = {Enrico Costanza and Joel E Fischer and James A Colley and Tom Rodden and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/361173/}, year = {2014}, date = {2014-01-01}, booktitle = {ACM CHI Conference on Human Factors in Computing Systems 2014}, pages = {813--822}, organization = {ACM}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Tran-Thanh, Long; Huynh, Trung Dong; Rosenfield, A; Ramchurn, Sarvapali; Jennings, Nicholas R BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees Inproceedings 13th International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2014. @inproceedings{eps362321, title = {BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees}, author = {Long Tran-Thanh and Trung Dong Huynh and A Rosenfield and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/362321/}, year = {2014}, date = {2014-01-01}, booktitle = {13th International Conference on Autonomous Agents and Multi-Agent Systems}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Vinyals, Meritxell; Macarthur, Kathryn; Farinelli, Alessandro; Ramchurn, Sarvapali; Jennings, Nicholas R A message-passing approach to decentralised parallel machine scheduling Journal Article The Computer Journal, 2014. @article{eps360818, title = {A message-passing approach to decentralised parallel machine scheduling}, author = {Meritxell Vinyals and Kathryn Macarthur and Alessandro Farinelli and Sarvapali Ramchurn and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/360818/}, year = {2014}, date = {2014-01-01}, journal = {The Computer Journal}, publisher = {Oxford University Press}, abstract = {This paper tackles the problem of parallelizing heterogeneous computational tasks across a number of computational nodes (aka agents) where each agent may not be able to perform all the tasks and may have different computational speeds. An equivalent problem can be found in operations research, and it is known as scheduling tasks on unrelated parallel machines (also known as R?Cmax). Given this equivalence observation, we present the spanning tree decentralized task distribution algorithm (ST-DTDA), the first decentralized solution to R?Cmax. ST-DTDA achieves decomposition by means of the min?max algorithm, a member of the generalized distributive law family, that performs inference by message-passing along the edges of a graphical model (known as a junction tree). Specifically, ST-DTDA uses min?max to optimally solve an approximation of the original R?Cmax problem that results from eliminating possible agent-task allocations until it is mapped into an acyclic structure. To eliminate those allocations that are least likely to have an impact on the solution quality, ST-DTDA uses a heuristic approach. Moreover, ST-DTDA provides a per-instance approximation ratio that guarantees that the makespan of its solution (optimal in the approximated R?Cmax problem) is not more than a factor ensuremathrho times the makespan of the optimal of the original problem. In our empirical evaluation of ST-DTDA, we show that ST-DTDA, with a min-regret heuristic, converges to solutions that are between 78 and 95% optimal whilst providing approximation ratios lower than 3.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper tackles the problem of parallelizing heterogeneous computational tasks across a number of computational nodes (aka agents) where each agent may not be able to perform all the tasks and may have different computational speeds. An equivalent problem can be found in operations research, and it is known as scheduling tasks on unrelated parallel machines (also known as R?Cmax). Given this equivalence observation, we present the spanning tree decentralized task distribution algorithm (ST-DTDA), the first decentralized solution to R?Cmax. ST-DTDA achieves decomposition by means of the min?max algorithm, a member of the generalized distributive law family, that performs inference by message-passing along the edges of a graphical model (known as a junction tree). Specifically, ST-DTDA uses min?max to optimally solve an approximation of the original R?Cmax problem that results from eliminating possible agent-task allocations until it is mapped into an acyclic structure. To eliminate those allocations that are least likely to have an impact on the solution quality, ST-DTDA uses a heuristic approach. Moreover, ST-DTDA provides a per-instance approximation ratio that guarantees that the makespan of its solution (optimal in the approximated R?Cmax problem) is not more than a factor ensuremathrho times the makespan of the optimal of the original problem. In our empirical evaluation of ST-DTDA, we show that ST-DTDA, with a min-regret heuristic, converges to solutions that are between 78 and 95% optimal whilst providing approximation ratios lower than 3. |
Fischer, J E; Jiang, W; Kerne, A; Greenhalgh, C; Ramchurn, Sarvapali D; Reece, Steven; Pantidi, N; Rodden, T Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game. Inproceedings 11th Int. Conference on the Design of Cooperative Systems (COOP ?14), 2014. @inproceedings{orchid192, title = {Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game.}, author = {J.E. Fischer and W Jiang and A Kerne and C Greenhalgh and Sarvapali D Ramchurn and Steven Reece and N Pantidi and T Rodden}, year = {2014}, date = {2014-01-01}, booktitle = {11th Int. Conference on the Design of Cooperative Systems (COOP ?14)}, howpublished = {http://www.orchid.ac.uk/eprints/192/1/COOP2014-Fischer-author-version.pdf}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Jiang, W; Fischer, J E; Greenhalgh, C; Ramchurn, Sarvapali D; Wu, Feng; Jennings, Nicholas R; Rodden, T Social Implications of Agent-based Planning Support for Human Teams. Inproceedings 2014 Int. Conference on Collaboration Technologies and Systems, 2014. @inproceedings{orchid191, title = {Social Implications of Agent-based Planning Support for Human Teams.}, author = {W Jiang and J.E. Fischer and C Greenhalgh and Sarvapali D Ramchurn and Feng Wu and Nicholas R Jennings and T Rodden}, year = {2014}, date = {2014-01-01}, booktitle = {2014 Int. Conference on Collaboration Technologies and Systems}, howpublished = {http://www.orchid.ac.uk/eprints/191/1/CTS2014-Jiang-author-version.pdf}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Pawlowski, Krzysztof; Kurach, Karol; Svensson, Kim; Ramchurn, Sarvapali D; Michalak, Tomasz; Rahwan, Talal Coalition Structure Generation with the Graphics Processing Unit Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, 2014. @inproceedings{orchid176, title = {Coalition Structure Generation with the Graphics Processing Unit}, author = {Krzysztof Pawlowski and Karol Kurach and Kim Svensson and Sarvapali D Ramchurn and Tomasz Michalak and Talal Rahwan}, year = {2014}, date = {2014-01-01}, booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Huynh, Trung Dong; Ebden, Mark; Ramchurn, Sarvapali; Roberts, Stephen; Moreau, Luc Data quality assessment from provenance graphs Inproceedings Provenance Analytics 2014, 2014. @inproceedings{eps365510, title = {Data quality assessment from provenance graphs}, author = {Trung Dong Huynh and Mark Ebden and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau}, url = {http://eprints.soton.ac.uk/365510/}, year = {2014}, date = {2014-01-01}, booktitle = {Provenance Analytics 2014}, abstract = {Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein. |
Jennings, Nicholas R; Moreau, Luc; Nicholson, D; Ramchurn, Sarvapali D; Roberts, S; Rodden, T; Rogers, Alex On human-agent collectives Journal Article Communications of the ACM, 57 (12), pp. 33-42, 2014. @article{eps364593, title = {On human-agent collectives}, author = {Nicholas R. Jennings and Luc Moreau and D Nicholson and Sarvapali D. Ramchurn and S Roberts and T Rodden and Alex Rogers}, url = {http://eprints.soton.ac.uk/364593/}, year = {2014}, date = {2014-01-01}, journal = {Communications of the ACM}, volume = {57}, number = {12}, pages = {33-42}, abstract = {We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented. |
Bistaffa, Filippo; Farinelli, Alessandro; Cerquides, Jesus; Rodriguez-Aguilar, Juan Antonio; Ramchurn, Sarvapali D Anytime Coalition Structure Generation on Synergy Graphs Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, pp. 13-20, 2014. @inproceedings{orchid175, title = {Anytime Coalition Structure Generation on Synergy Graphs}, author = {Filippo Bistaffa and Alessandro Farinelli and Jesus Cerquides and Juan Antonio Rodriguez-Aguilar and Sarvapali D Ramchurn}, url = {http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems}, pages = {13-20}, abstract = {We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents). |
2013 |
Alam, Muddasser; Alan, Alper; Rogers, Alex; Ramchurn, Sarvapali D Towards a smart home framework Inproceedings 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013), 2013. @inproceedings{eps357187, title = {Towards a smart home framework}, author = {Muddasser Alam and Alper Alan and Alex Rogers and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/357187/}, year = {2013}, date = {2013-01-01}, booktitle = {5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013)}, abstract = {We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work. |
Alam, Muddasser; Ramchurn, Sarvapali; Rogers, Alex Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013. @inproceedings{eps346637, title = {Cooperative energy exchange for the efficient use of energy and resources in remote communities. [Winner, Best Student Paper Award at AAMAS2013]}, author = {Muddasser Alam and Sarvapali Ramchurn and Alex Rogers}, url = {http://eprints.soton.ac.uk/346637/}, year = {2013}, date = {2013-01-01}, booktitle = {Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), 2013. @inproceedings{eps350941, title = {Interdependent multi-issue negotiation for energy exchange in remote communities}, author = {Muddasser Alam and Alex Rogers and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/350941/}, year = {2013}, date = {2013-01-01}, booktitle = {Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13)}, 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 negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%}, 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 negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37% |
Alam, Muddasser; Rogers, Alex; Ramchurn, Sarvapali D Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013. @inproceedings{eps357186, title = {Interdependent multi-issue negotiation for energy exchange in remote communities}, author = {Muddasser Alam and Alex Rogers and Sarvapali D. Ramchurn}, url = {http://eprints.soton.ac.uk/357186/}, year = {2013}, date = {2013-01-01}, booktitle = {International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Cerquides, Jesus; Farinelli, Alessandro; Meseguer, Pedro; Ramchurn, Sarvapali A tutorial on optimisation for multi-agent systems Journal Article The Computer Journal, pp. 1–26, 2013. @article{eps361998, title = {A tutorial on optimisation for multi-agent systems}, author = {Jesus Cerquides and Alessandro Farinelli and Pedro Meseguer and Sarvapali Ramchurn}, url = {http://eprints.soton.ac.uk/361998/}, year = {2013}, date = {2013-01-01}, journal = {The Computer Journal}, pages = {1--26}, abstract = {Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization}, keywords = {}, pubstate = {published}, tppubtype = {article} } Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization |
Farinelli, Alessandro; Bicego, Manuele; Ramchurn, Sarvapali; Zuchelli, Marco C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation Inproceedings 23rd International Joint Conference on Artificial Intelligence, AAAI Press / International Joint Conferences on Artificial Intelligence, 2013. @inproceedings{eps351521, title = {C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation}, author = {Alessandro Farinelli and Manuele Bicego and Sarvapali Ramchurn and Marco Zuchelli}, url = {http://eprints.soton.ac.uk/351521/}, year = {2013}, date = {2013-01-01}, booktitle = {23rd International Joint Conference on Artificial Intelligence}, publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence}, abstract = {Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes. |
Fischer, Joel E; Ramchurn, Sarvapali D; Osborne, Michael A; Parson, Oliver; Huynh, Trung Dong; Alam, Muddasser; Pantidi, Nadia; Moran, Stuart; Bachour, Khaled; Reece, Steven; Costanza, Enrico; Rodden, Tom; Jennings, Nicholas R Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Inproceedings International Conference on Intelligent User Interfaces, pp. 383–394, 2013. @inproceedings{eps346991, title = {Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling}, author = {Joel E. Fischer and Sarvapali D. Ramchurn and Michael A. Osborne and Oliver Parson and Trung Dong Huynh and Muddasser Alam and Nadia Pantidi and Stuart Moran and Khaled Bachour and Steven Reece and Enrico Costanza and Tom Rodden and Nicholas R. Jennings}, url = {http://eprints.soton.ac.uk/346991/}, year = {2013}, date = {2013-01-01}, booktitle = {International Conference on Intelligent User Interfaces}, pages = {383--394}, abstract = {We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users\' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household\'s energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We present a system and study of personalized energy-related recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management. |
Huynh, Trung Dong; Ebden, Mark; Venanzi, Matteo; Ramchurn, Sarvapali; Roberts, Stephen; Moreau, Luc Interpretation of Crowdsourced Activities Using Provenance Network Analysis Inproceedings The First AAAI Conference on Human Computation and Crowdsourcing, Association for the Advancement of Artificial Intelligence, 2013. @inproceedings{eps357199, title = {Interpretation of Crowdsourced Activities Using Provenance Network Analysis}, author = {Trung Dong Huynh and Mark Ebden and Matteo Venanzi and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau}, url = {http://eprints.soton.ac.uk/357199/}, year = {2013}, date = {2013-01-01}, booktitle = {The First AAAI Conference on Human Computation and Crowdsourcing}, publisher = {Association for the Advancement of Artificial Intelligence}, abstract = {Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein. |
Kleiner, Alexander; Farinelli, Alessandro; Ramchurn, Sarvapali; Shi, Bing; Mafioletti, Fabio; Refatto, Riccardo RMASBench: a benchmarking system for multi-agent coordination in urban search and rescue Inproceedings International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2013), 2013. @inproceedings{eps350678, title = {RMASBench: a benchmarking system for multi-agent coordination in urban search and rescue}, author = {Alexander Kleiner and Alessandro Farinelli and Sarvapali Ramchurn and Bing Shi and Fabio Mafioletti and Riccardo Refatto}, url = {http://eprints.soton.ac.uk/350678/}, year = {2013}, date = {2013-01-01}, booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2013)}, abstract = {This demonstration paper illustrates RMASBench, a new benchmarking system based on the RoboCup Rescue Agent simulator. The aim of the system is to facilitate benchmarking of coordination approaches in controlled settings for dynamic rescue scenario. In particular, the key features of the systems are: i) programming interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents? behaviors, ii) implementations of state-of-the art coordination approaches: DSA and MaxSum, iii) a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behaviour of thousands of agents in real time.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This demonstration paper illustrates RMASBench, a new benchmarking system based on the RoboCup Rescue Agent simulator. The aim of the system is to facilitate benchmarking of coordination approaches in controlled settings for dynamic rescue scenario. In particular, the key features of the systems are: i) programming interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents? behaviors, ii) implementations of state-of-the art coordination approaches: DSA and MaxSum, iii) a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behaviour of thousands of agents in real time. |
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen AgentSwitch: towards smart electricity tariff selection Inproceedings 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013. @inproceedings{eps349815, title = {AgentSwitch: towards smart electricity tariff selection}, author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts}, url = {http://eprints.soton.ac.uk/349815/}, year = {2013}, date = {2013-01-01}, booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability. |
Ramchurn, Sarvapali D; Huynh, Trung Dong; Venanzi, Matteo; Shi, Bing Collabmap: crowdsourcing maps for emergency planning Inproceedings The 5th Annual ACM Web Science Conference, pp. 326–335, 2013. @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} } 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. |
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick; Koutitas, Georgios Congestion management for urban EV charging systems Inproceedings 4th IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, 2013. @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 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. |
Svensson, Kim; Ramchurn, Sarvapali; Cruz, Francisco; Rodriguez-Aguilar, Juan-Antonio; Cerquides, Jesus Solving the coalition structure generation problem on a GPU Inproceedings 6th International Workshop on Optimisation in Multi-Agent Systems, 2013. @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} } 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. |
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D Forecasting multi-appliance usage for smart home energy management Inproceedings 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013. @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} } |
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D Activity prediction for agent-based home energy management Inproceedings Agent Technologies for Energy Systems (ATES 2013), 2013. @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} } |
Publications
2016 |
It is too hot: an in-situ study of three designs for heating Inproceedings The SIGCHI Conference on Human Factors in Computing Systems, 2016. |
An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid Inproceedings Proceedings of 30th AAAI Conference on Artificial Intelligence (AAAI), AAAI Press, 2016. |
Managing energy markets in future smart grids using bilateral contracts Inproceedings European Conference on Artificial Intelligence (ECAI), The Hague, Netherlands, 2016. |
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article IEEE Trans. Cybernetics, 46 (12), pp. 3364–3376, 2016. |
Towards an optimal EV charging scheduling scheme with V2G and V2V energy transfer Inproceedings 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, November 6-9, 2016, pp. 302–307, 2016. |
2015 |
CrowdAR: augmenting live video with a real-time crowd Inproceedings HCOMP 2015: Third AAAI Conference on Human Computation and Crowdsourcing, 2015. |
Recommending Fair Payments for Large-Scale Social Ridesharing Inproceedings ACM Conference on Recommender Systems (Recsys), 2015. |
Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings 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. |
Managing energy tariffs with agents: a field study of a future smart energy system at home Inproceedings 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. |
Building a Bird's Eye View: Collaborative Work Inproceedings Proceedings of SIGCHI (To appear), 2015. |
Crowdsourcing Complex Workflows under Budget Constraints Inproceedings Proceedings of the AAAI Conference, AAAI, 2015. |
Sharing Rides with Friends: a Coalition Formation Algorithm for Ridesharing Inproceedings Proceedings of the AAAI Conference, 2015. |
Balanced Trade Reduction for Dual-Role Exchange Markets Inproceedings Proceedings of the AAAI Conference, 2015. |
Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey Journal Article IEEE Transactions on Intelligent Transportation Systems, 2015. |
Real-time opinion aggregation methods for crowd robotics Inproceedings Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015. |
HAC-ER: A disaster response system based on human-agent collectives Inproceedings 14th International Conference on Autonomous Agents and Multi-Agent Systems, 2015. |
Human-agent collaboration for disaster response Journal Article Journal of Autonomous Agents and Multi-Agent Systems, pp. 1–30, 2015. |
A scalable, decentralised multi-issue negotiation protocol for energy exchange Inproceedings International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
Agile Planning for Real-World Disaster Response Inproceedings International Joint Conference on Artificial Intelligence, 2015. |
A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments Inproceedings International Joint Conference on Artificial Intelligence, 2015. |
Consumer Targeting in Residential Demand Response Programmes Inproceedings 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. |
Multi-Agent Patrolling under Uncertainty and Threats Journal Article PLoS ONE, 10 (6), pp. e0130154, 2015, ISBN: 1932-6203. |
Decentralized Patrolling Under Constraints in Dynamic Environments Journal Article Cybernetics, IEEE Transactions on, PP (99), pp. 1-13, 2015, ISSN: 2168-2267. |
ACM, 2015, ISBN: 978-1-4503-3609-3. |
2014 |
Behavioural biometrics using electricity load profiles Journal Article Proceedings of the International Conference on Pattern Recognition, 2014. |
A field study of human-agent interaction for electricity tariff switching Inproceedings Proceedings of the 13th International Conference on Autonomous Agents and Multi-Agent Systems, 2014. |
Doing the laundry with agents: a field trial of a future smart energy system in the home Inproceedings ACM CHI Conference on Human Factors in Computing Systems 2014, pp. 813–822, ACM 2014. |
BudgetFix: budget limited crowdsourcing for interdependent task allocation with quality guarantees Inproceedings 13th International Conference on Autonomous Agents and Multi-Agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2014. |
A message-passing approach to decentralised parallel machine scheduling Journal Article The Computer Journal, 2014. |
Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game. Inproceedings 11th Int. Conference on the Design of Cooperative Systems (COOP ?14), 2014. |
Social Implications of Agent-based Planning Support for Human Teams. Inproceedings 2014 Int. Conference on Collaboration Technologies and Systems, 2014. |
Coalition Structure Generation with the Graphics Processing Unit Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, 2014. |
Data quality assessment from provenance graphs Inproceedings Provenance Analytics 2014, 2014. |
On human-agent collectives Journal Article Communications of the ACM, 57 (12), pp. 33-42, 2014. |
Anytime Coalition Structure Generation on Synergy Graphs Inproceedings 13th Int. Conf. on Autonomous Agents and Multi-Agent Systems, pp. 13-20, 2014. |
2013 |
Towards a smart home framework Inproceedings 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013), 2013. |
Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013. |
Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13), 2013. |
Interdependent multi-issue negotiation for energy exchange in remote communities Inproceedings International Workshop on AI Problems and Approaches for Intelligent Environments (AI4IE), 2013. |
A tutorial on optimisation for multi-agent systems Journal Article The Computer Journal, pp. 1–26, 2013. |
C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation Inproceedings 23rd International Joint Conference on Artificial Intelligence, AAAI Press / International Joint Conferences on Artificial Intelligence, 2013. |
Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling Inproceedings International Conference on Intelligent User Interfaces, pp. 383–394, 2013. |
Interpretation of Crowdsourced Activities Using Provenance Network Analysis Inproceedings The First AAAI Conference on Human Computation and Crowdsourcing, Association for the Advancement of Artificial Intelligence, 2013. |
RMASBench: a benchmarking system for multi-agent coordination in urban search and rescue Inproceedings International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2013), 2013. |
AgentSwitch: towards smart electricity tariff selection Inproceedings 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013. |
Collabmap: crowdsourcing maps for emergency planning Inproceedings The 5th Annual ACM Web Science Conference, pp. 326–335, 2013. |
Congestion management for urban EV charging systems Inproceedings 4th IEEE International Conference on Smart Grid Communications (SmartGridComm), IEEE, 2013. |
Solving the coalition structure generation problem on a GPU Inproceedings 6th International Workshop on Optimisation in Multi-Agent Systems, 2013. |
Forecasting multi-appliance usage for smart home energy management Inproceedings 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), 2013. |
Activity prediction for agent-based home energy management Inproceedings Agent Technologies for Energy Systems (ATES 2013), 2013. |
