Gopal
Gopal
Gopal
Gopal
Gopal
Gopal
Gopal
Gopal
Gopal
Gopal
font
Filter
×
Sorry, no publications matched your criteria.
141.
Huynh, Trung Dong; Ebden, Mark; Venanzi, Matteo; Ramchurn, Sarvapali; Roberts, Stephen; Moreau, Luc
Interpretation of Crowdsourced Activities Using Provenance Network Analysis Proceedings Article
In: 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.
142.
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 Proceedings Article
In: 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.
143.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
144.
Ramchurn, Sarvapali D.; Huynh, Trung Dong; Venanzi, Matteo; Shi, Bing
Collabmap: crowdsourcing maps for emergency planning Proceedings Article
In: 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.
145.
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick; Koutitas, Georgios
Congestion management for urban EV charging systems Proceedings Article
In: 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.
146.
Svensson, Kim; Ramchurn, Sarvapali; Cruz, Francisco; Rodriguez-Aguilar, Juan-Antonio; Cerquides, Jesus
Solving the coalition structure generation problem on a GPU Proceedings Article
In: 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.
147.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 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}
}
148.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, D. Sarvapali
Activity prediction for agent-based home energy management Proceedings Article
In: 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}
}
149.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
150.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
151.
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
152.
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
153.
Miller, Sam; Ramchurn, Sarvapali D; Rogers, Alex
Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid Journal Article
In: In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2012.
@article{eps273142,
title = {Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid},
author = {Sam Miller and Sarvapali D Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/273142/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
journal = {In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
abstract = {Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.
154.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
155.
Ramchurn, Sarvapali D.; Gerding, Enrico; Jennings, N. R.; Hu, Jun
Practical distributed coalition formation via heuristic negotiation in social networks Proceedings Article
In: Fifth International Workshop on Optimisation in Multi-Agent Systems (OPTMAS), 2012.
@inproceedings{eps344492,
title = {Practical distributed coalition formation via heuristic negotiation in social networks},
author = {Sarvapali D. Ramchurn and Enrico Gerding and N. R. Jennings and Jun Hu},
url = {http://eprints.soton.ac.uk/344492/},
year = {2012},
date = {2012-01-01},
booktitle = {Fifth International Workshop on Optimisation in Multi-Agent Systems (OPTMAS)},
abstract = {We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.
156.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
157.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
158.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Predicting energy consumption activities for home energy management Proceedings Article
In: Agent Technologies for Energy Systems (ATES 2012), 2012.
@inproceedings{eps339215,
title = {Predicting energy consumption activities for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/339215/},
year = {2012},
date = {2012-01-01},
booktitle = {Agent Technologies for Energy Systems (ATES 2012)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
159.
Voice, Thomas; Ramchurn, Sarvapali; Jennings, Nick
On coalition formation with sparse synergies Proceedings Article
In: Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp. 223–230, 2012.
@inproceedings{eps273083,
title = {On coalition formation with sparse synergies},
author = {Thomas Voice and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/273083/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
pages = {223–230},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
160.
Simpson, Edwin; Reece, Steven; Penta, Antonio; Ramchurn, Sarvapali D
Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses Proceedings Article
In: Proceedings of The Twenty-First Text REtrieval Conference, TREC 2012, Gaithersburg, Maryland, USA, November 6-9, 2012, 2012.
@inproceedings{DBLP:conf/trec/SimpsonRPR12,
title = {Using a Bayesian Model to Combine LDA Features with Crowdsourced
Responses},
author = {Edwin Simpson and Steven Reece and Antonio Penta and Sarvapali D Ramchurn},
url = {http://trec.nist.gov/pubs/trec21/papers/HAC.crowd.final.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of The Twenty-First Text REtrieval Conference, TREC
2012, Gaithersburg, Maryland, USA, November 6-9, 2012},
crossref = {DBLP:conf/trec/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sorry, no publications matched your criteria.
141.
Huynh, Trung Dong; Ebden, Mark; Venanzi, Matteo; Ramchurn, Sarvapali; Roberts, Stephen; Moreau, Luc
Interpretation of Crowdsourced Activities Using Provenance Network Analysis Proceedings Article
In: 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.
142.
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 Proceedings Article
In: 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.
143.
Ramchurn, Sarvapali; Osborne, Michael; Parson, Oliver; Rahwan, Talal; Maleki, Sasan; Reece, Steve; Huynh, Trung Dong; Alam, Muddasser; Fischer, Joel; Rodden, Tom; Moreau, Luc; Roberts, Sephen
AgentSwitch: towards smart electricity tariff selection Proceedings Article
In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), International Foundation for Autonomous Agents and Multiagent Systems, 2013.
@inproceedings{eps349815,
title = {AgentSwitch: towards smart electricity tariff selection},
author = {Sarvapali Ramchurn and Michael Osborne and Oliver Parson and Talal Rahwan and Sasan Maleki and Steve Reece and Trung Dong Huynh and Muddasser Alam and Joel Fischer and Tom Rodden and Luc Moreau and Sephen Roberts},
url = {http://eprints.soton.ac.uk/349815/},
year = {2013},
date = {2013-01-01},
booktitle = {12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present AgentSwitch, a prototype agent-based platform to solve the electricity tariff selection problem. AgentSwitch incorporates novel algorithms to make predictions of hourly energy usage as well as detect (and suggest to the user) deferrable loads that could be shifted to off-peak times to maximise savings. To take advantage of group discounts from energy retailers, we develop a new scalable collective energy purchasing mechanism, based on the Shapley value, that ensures individual members of a collective (interacting through AgentSwitch) fairly share the discounts. To demonstrate the effectiveness of our algorithms we empirically evaluate them individually on real-world data (with up to 3000 homes in the UK) and show that they outperform the state of the art in their domains. Finally, to ensure individual components are accountable in providing recommendations, we provide a novel provenance-tracking service to record the ?ow of data in the system, and therefore provide users with a means of checking the provenance of suggestions from AgentSwitch and assess their reliability.
144.
Ramchurn, Sarvapali D.; Huynh, Trung Dong; Venanzi, Matteo; Shi, Bing
Collabmap: crowdsourcing maps for emergency planning Proceedings Article
In: 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.
145.
Rigas, Emmanouil; Ramchurn, Sarvapali; Bassiliades, Nick; Koutitas, Georgios
Congestion management for urban EV charging systems Proceedings Article
In: 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.
146.
Svensson, Kim; Ramchurn, Sarvapali; Cruz, Francisco; Rodriguez-Aguilar, Juan-Antonio; Cerquides, Jesus
Solving the coalition structure generation problem on a GPU Proceedings Article
In: 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.
147.
Truong, Ngoc Cuong; McInerney, James; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Forecasting multi-appliance usage for smart home energy management Proceedings Article
In: 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}
}
148.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, D. Sarvapali
Activity prediction for agent-based home energy management Proceedings Article
In: 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}
}
149.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Towards appliance usage prediction for home energy management Proceedings Article
In: ACM E-Energy 2013, 2013.
@inproceedings{eps351240,
title = {Towards appliance usage prediction for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/351240/},
year = {2013},
date = {2013-01-01},
booktitle = {ACM E-Energy 2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
150.
Costanza, Enrico; Ramchurn, Sarvapali D.; Jennings, Nicholas R.
Understanding domestic energy consumption through interactive visualisation: a field study Proceedings Article
In: UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 216–225, 2012.
@inproceedings{eps338804,
title = {Understanding domestic energy consumption through interactive visualisation: a field study},
author = {Enrico Costanza and Sarvapali D. Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/338804/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12. Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {216–225},
abstract = {Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Motivated by the need to better manage energy demand in the home, in this paper we advocate the integration into Ubicomp systems of interactive energy consumption visualisations, that allow users to engage with and understand their consumption data, relating it to concrete activities in their life. To this end, we present the design, implementation, and evaluation of FigureEnergy, a novel interactive visualisation that allows users to annotate and manipulate a graphical representation of their own electricity consumption data, and therefore make sense of their past energy usage and understand when, how, and to what end, some amount of energy was used. To validate our design, we deployed FigureEnergy ?in the wild? ? 12 participants installed meters in their homes and used the system for a period of two weeks. The results suggest that the annotation approach is successful overall: by engaging with the data users started to relate energy consumption to activities rather than just to appliances. Moreover, they were able to discover that some appliances consume more than they expected, despite having had prior experience of using other electricity displays.
151.
Ebden, Mark; Huynh, Trung Dong; Moreau, Luc; Ramchurn, Sarvapali; Stephen, Roberts
Network analysis on provenance graphs from a crowdsourcing application Proceedings Article
In: Groth, Paul; Frew, James (Ed.): 4th International Provenance and Annotation Workshop, pp. 168–182, 2012.
@inproceedings{eps340068,
title = {Network analysis on provenance graphs from a crowdsourcing application},
author = {Mark Ebden and Trung Dong Huynh and Luc Moreau and Sarvapali Ramchurn and Roberts Stephen},
editor = {Paul Groth and James Frew},
url = {http://eprints.soton.ac.uk/340068/},
year = {2012},
date = {2012-01-01},
booktitle = {4th International Provenance and Annotation Workshop},
volume = {7525},
pages = {168–182},
series = {0302-9743},
abstract = {Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Crowdsourcing has become a popular means for quickly achieving various tasks in large quantities. CollabMap is an online mapping application in which we crowdsource the identification of evacuation routes in residential areas to be used for planning large-scale evacuations. So far, approximately 38,000 micro-tasks have been completed by over 100 contributors. In order to assist with data verification, we introduced provenance tracking into the application, and approximately 5,000 provenance graphs have been generated. They have provided us various insights into the typical characteristics of provenance graphs in the crowdsourcing context. In particular, we have estimated probability distribution functions over three selected characteristics of these provenance graphs: the node degree, the graph diameter, and the densification exponent. We describe methods to define these three characteristics across specific combinations of node types and edge types, and present our findings in this paper. Applications of our methods include rapid comparison of one provenance graph versus another, or of one style of provenance database versus another. Our results also indicate that provenance graphs represent a suitable area of exploitation for existing network analysis tools concerned with modelling, prediction, and the inference of missing nodes and edges.
152.
Matthews, Tim; Ramchurn, Sarvapali; Chalkiadakis, Georgios
Competing with humans at fantasy football: team formation in large partially-observable domains Proceedings Article
In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence, pp. 1394–1400, Association for the Advancement of Artificial Intelligence, 2012.
@inproceedings{eps340382,
title = {Competing with humans at fantasy football: team formation in large partially-observable domains},
author = {Tim Matthews and Sarvapali Ramchurn and Georgios Chalkiadakis},
url = {http://eprints.soton.ac.uk/340382/},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Twenty-Sixth Conference on Artificial Intelligence},
pages = {1394–1400},
publisher = {Association for the Advancement of Artificial Intelligence},
abstract = {We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present the first real-world benchmark for sequentially optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker?s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers? performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players
153.
Miller, Sam; Ramchurn, Sarvapali D; Rogers, Alex
Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid Journal Article
In: In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2012.
@article{eps273142,
title = {Optimal Decentralised Dispatch of Embedded Generation in the Smart Grid},
author = {Sam Miller and Sarvapali D Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/273142/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
journal = {In Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
abstract = {Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Distribution network operators face a number of challenges; capacity constrained networks, and balancing electricity demand with generation from intermittent renewable resources. Thus, there is an increasing need for scalable approaches to facilitate optimal dispatch in the distribution network. To this end, we cast the optimal dispatch problem as a decentralised agent-based coordination problem and formalise it as a DCOP. We show how this can be decomposed as a factor graph and solved in a decentralised manner using algorithms based on the generalised distributive law; in particular, the max-sum algorithm. We go on to show that max-sum applied na?vely in this setting performs a large number of redundant computations. To address this issue, we present a novel decentralised message passing algorithm using dynamic programming that outperforms max-sum by pruning the search space. We empirically evaluate our algorithm using real distribution network data, showing that it outperforms (in terms of computational time and total size of messages sent) both a centralised approach, which uses IBM?s ILOG CPLEX 12.2, and max-sum, for large networks.
154.
Ramchurn, Sarvapali; Vytelingum, Perukrishnen; Rogers, Alex; Jennings, Nicholas R.
Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence Journal Article
In: Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012.
@article{eps272606,
title = {Putting the Smarts into the Smart Grid: A Grand Challenge for Artificial Intelligence},
author = {Sarvapali Ramchurn and Perukrishnen Vytelingum and Alex Rogers and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/272606/},
year = {2012},
date = {2012-01-01},
journal = {Communications of the ACM},
volume = {55},
number = {4},
pages = {86–97},
publisher = {ACM},
abstract = {The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The phenomenal growth in material wealth experienced in developed countries throughout the twentieth century has largely been driven by the availability of cheap energy derived from fossil fuels (originally coal, then oil, and most recently natural gas). However, the continued availability of this cheap energy cannot be taken for granted given the growing concern that increasing demand for these fuels (and particularly, demand for oil) will outstrip our ability to produce them (so called `peak oil'). Many mature oil and gas fields around the world have already peaked and their annual production is now steadily declining. Predictions of when world oil production will peak vary between 0-20 years into the future, but even the most conservative estimates provide little scope for complacency given the significant price increases that peak oil is likely to precipitate. Furthermore, many of the oil and gas reserves that do remain are in environmentally or politically sensitive regions of the world where threats to supply create increased price volatility (as evidenced by the 2010 Deepwater Horizon disaster and 2011 civil unrest in the Middle East). Finally, the growing consensus on the long term impact of carbon emissions from burning fossil fuels suggests that even if peak oil is avoided, and energy security assured, a future based on fossil fuel use will expose regions of the world to damaging climate change that will make the lives of many of the world's poorest people even harder.
155.
Ramchurn, Sarvapali D.; Gerding, Enrico; Jennings, N. R.; Hu, Jun
Practical distributed coalition formation via heuristic negotiation in social networks Proceedings Article
In: Fifth International Workshop on Optimisation in Multi-Agent Systems (OPTMAS), 2012.
@inproceedings{eps344492,
title = {Practical distributed coalition formation via heuristic negotiation in social networks},
author = {Sarvapali D. Ramchurn and Enrico Gerding and N. R. Jennings and Jun Hu},
url = {http://eprints.soton.ac.uk/344492/},
year = {2012},
date = {2012-01-01},
booktitle = {Fifth International Workshop on Optimisation in Multi-Agent Systems (OPTMAS)},
abstract = {We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a novel framework for decentralised coalition formation in social networks, where agents can form coalitions through bilateral negotiations with their neighbours. Specifically, we present a practical negotiation protocol and decision functions that enable agents to form coalitions with agents beyond their peers. Building on this, we establish baseline negotiation strategies which we empirically show to be efficient (agreements are reached in few negotiation rounds) and effective (agreements have high utility compared to a centralised approach) on a variety of network topologies. Moreover, we show that the average degree of social networks can significantly affect the performance of these strategies.
156.
Richardson, Darren P.; Costanza, Enrico; Ramchurn, Sarvapali D.
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid Proceedings Article
In: UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 613–614, 2012.
@inproceedings{eps349083,
title = {Evaluating semi-automatic annotation of domestic energy consumption as a memory aid},
author = {Darren P. Richardson and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/349083/},
year = {2012},
date = {2012-01-01},
booktitle = {UbiComp '12 Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
pages = {613–614},
abstract = {Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.
157.
Rogers, Alex; Ramchurn, Sarvapali; Jennings, Nicholas R.
Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research Proceedings Article
In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12), pp. 2166–2172, 2012.
@inproceedings{eps337560,
title = {Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research},
author = {Alex Rogers and Sarvapali Ramchurn and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/337560/},
year = {2012},
date = {2012-01-01},
booktitle = {Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12)},
pages = {2166–2172},
abstract = {Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Restructuring electricity grids to meet the increased demand caused by the electrification of transport and heating, while making greater use of intermittent renewable energy sources, represents one of the greatest engineering challenges of our day. This modern electric- ity grid, in which both electricity and information flow in two directions between large numbers of widely dis- tributed suppliers and generators – commonly termed the ?smart grid? – represents a radical reengineering of infrastructure which has changed little over the last hundred years. However, the autonomous behaviour expected of the smart grid, its distributed nature, and the existence of multiple stakeholders each with their own incentives and interests, challenges existing engineering approaches. In this challenge paper, we describe why we believe that artificial intelligence, and particularly, the fields of autonomous agents and multi-agent systems are essential for delivering the smart grid as it is envisioned. We present some recent work in this area and describe many of the challenges that still remain.
158.
Truong, Ngoc Cuong; Tran-Thanh, Long; Costanza, Enrico; Ramchurn, Sarvapali D.
Predicting energy consumption activities for home energy management Proceedings Article
In: Agent Technologies for Energy Systems (ATES 2012), 2012.
@inproceedings{eps339215,
title = {Predicting energy consumption activities for home energy management},
author = {Ngoc Cuong Truong and Long Tran-Thanh and Enrico Costanza and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/339215/},
year = {2012},
date = {2012-01-01},
booktitle = {Agent Technologies for Energy Systems (ATES 2012)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
159.
Voice, Thomas; Ramchurn, Sarvapali; Jennings, Nick
On coalition formation with sparse synergies Proceedings Article
In: Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp. 223–230, 2012.
@inproceedings{eps273083,
title = {On coalition formation with sparse synergies},
author = {Thomas Voice and Sarvapali Ramchurn and Nick Jennings},
url = {http://eprints.soton.ac.uk/273083/},
year = {2012},
date = {2012-01-01},
booktitle = {Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
pages = {223–230},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
160.
Simpson, Edwin; Reece, Steven; Penta, Antonio; Ramchurn, Sarvapali D
Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses Proceedings Article
In: Proceedings of The Twenty-First Text REtrieval Conference, TREC 2012, Gaithersburg, Maryland, USA, November 6-9, 2012, 2012.
@inproceedings{DBLP:conf/trec/SimpsonRPR12,
title = {Using a Bayesian Model to Combine LDA Features with Crowdsourced
Responses},
author = {Edwin Simpson and Steven Reece and Antonio Penta and Sarvapali D Ramchurn},
url = {http://trec.nist.gov/pubs/trec21/papers/HAC.crowd.final.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of The Twenty-First Text REtrieval Conference, TREC
2012, Gaithersburg, Maryland, USA, November 6-9, 2012},
crossref = {DBLP:conf/trec/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Selected Publications
Multi-agent signal-less intersection management with dynamic platoon formation
No data available.
https://eprints.soton.ac.uk/478647/
@inproceedings{soton478647,
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Multi-agent signal-less intersection management with dynamic platoon formation},
author = {Phuriwat Worrawichaipat and Enrico Gerding and Ioannis Kaparias and Sarvapali Ramchurn},
url = {https://eprints.soton.ac.uk/478647/},
year = {2023},
date = {2023-05-01},
booktitle = {22nd International Conference on Autonomous Agents and Multiagent Systems (29/05/23 - 02/06/23)},
pages = {1542--1550},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
AI Foundation Models: initial review, CMA Consultation, TAS Hub Response
No data available.
https://eprints.soton.ac.uk/477553/
@misc{soton477553,
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
title = {AI Foundation Models: initial review, CMA Consultation, TAS Hub Response},
author = {Joshua Krook and Derek McAuley and Stuart Anderson and John Downer and Peter Winter and Sarvapali D Ramchurn},
url = {https://eprints.soton.ac.uk/477553/},
year = {2023},
date = {2023-06-01},
publisher = {University of Southampton},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The effect of data visualisation quality and task density on human-swarm interaction
No data available.
https://eprints.soton.ac.uk/479970/
@inproceedings{soton479970,
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {The effect of data visualisation quality and task density on human-swarm interaction},
author = {Ayodeji Abioye and Mohammad Naiseh and William Hunt and Jediah R Clark and Sarvapali D Ramchurn and Mohammad Soorati},
url = {https://eprints.soton.ac.uk/479970/},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings of the 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
publisher = {IEEE},
abstract = {Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system - the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Demonstrating performance benefits of human-swarm teaming
Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.
https://eprints.soton.ac.uk/479903/
@inproceedings{soton479903,
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
title = {Demonstrating performance benefits of human-swarm teaming},
author = {William Hunt and Jack Ryan and Ayodeji O Abioye and Sarvapali D Ramchurn and Mohammad D Soorati},
url = {https://eprints.soton.ac.uk/479903/},
year = {2023},
date = {2023-05-01},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {3062--3064},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {Autonomous swarms of robots can bring robustness, scalability and adaptability to safety-critical tasks such as search and rescue but their application is still very limited. Using semi-autonomous swarms with human control can bring robot swarms to real-world applications. Human operators can define goals for the swarm, monitor their performance and interfere with, or overrule, the decisions and behaviour. We present the "Human And Robot Interactive Swarm'' simulator (HARIS) that allows multi-user interaction with a robot swarm and facilitates qualitative and quantitative user studies through simulation of robot swarms completing tasks, from package delivery to search and rescue, with varying levels of human control. In this demonstration, we showcase the simulator by using it to study the performance gain offered by maintaining a "human-in-the-loop'' over a fully autonomous system as an example. This is illustrated in the context of search and rescue, with an autonomous allocation of resources to those in need.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sorry, no publications matched your criteria.