No data available.

No data available.

@inproceedings{orchid192,
title = {Supporting Team Coordination on the Ground: Requirements from a Mixed Reality Game.},
author = {J.E. Fischer and W Jiang and A Kerne and C Greenhalgh and Sarvapali D Ramchurn and Steven Reece and N Pantidi and T Rodden},
year = {2014},
date = {2014-01-01},
booktitle = {11th Int. Conference on the Design of Cooperative Systems (COOP ?14)},
howpublished = {http://www.orchid.ac.uk/eprints/192/1/COOP2014-Fischer-author-version.pdf},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

No data available.

No data available.

@inproceedings{orchid191,
title = {Social Implications of Agent-based Planning Support for Human Teams.},
author = {W Jiang and J.E. Fischer and C Greenhalgh and Sarvapali D Ramchurn and Feng Wu and Nicholas R Jennings and T Rodden},
year = {2014},
date = {2014-01-01},
booktitle = {2014 Int. Conference on Collaboration Technologies and Systems},
howpublished = {http://www.orchid.ac.uk/eprints/191/1/CTS2014-Jiang-author-version.pdf},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

No data available.

No data available.

@inproceedings{orchid176,
title = {Coalition Structure Generation with the Graphics Processing Unit},
author = {Krzysztof Pawlowski and Karol Kurach and Kim Svensson and Sarvapali D Ramchurn and Tomasz Michalak and Talal Rahwan},
year = {2014},
date = {2014-01-01},
booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.

http://eprints.soton.ac.uk/365510/

@inproceedings{eps365510, title = {Data quality assessment from provenance graphs}, author = {Trung Dong Huynh and Mark Ebden and Sarvapali Ramchurn and Stephen Roberts and Luc Moreau}, url = {http://eprints.soton.ac.uk/365510/}, year = {2014}, date = {2014-01-01}, booktitle = {Provenance Analytics 2014}, abstract = {Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer data quality. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing data based on the network metrics of provenance graphs to learn about such patterns and to relate them to data quality in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} }

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.

http://eprints.soton.ac.uk/364593/

@article{eps364593,
title = {On human-agent collectives},
author = {Nicholas R. Jennings and Luc Moreau and D Nicholson and Sarvapali D. Ramchurn and S Roberts and T Rodden and Alex Rogers},
url = {http://eprints.soton.ac.uk/364593/},
year = {2014},
date = {2014-01-01},
journal = {Communications of the ACM},
volume = {57},
number = {12},
pages = {33-42},
abstract = {We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People?s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented.},
keywords = {},
pubstate = {published},
tppubtype = {article} }

We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents).

http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf

@inproceedings{orchid175,
title = {Anytime Coalition Structure Generation on Synergy Graphs},
author = {Filippo Bistaffa and Alessandro Farinelli and Jesus Cerquides and Juan Antonio Rodriguez-Aguilar and Sarvapali D Ramchurn},
url = {http://aamas2014.lip6.fr/proceedings/aamas/p13.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {13th Int. Conf. on Autonomous Agents and Multi-Agent Systems},
pages = {13-20},
abstract = {We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS),
which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial).
Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.

http://eprints.soton.ac.uk/357187/

@inproceedings{eps357187,
title = {Towards a smart home framework},
author = {Muddasser Alam and Alper Alan and Alex Rogers and Sarvapali D. Ramchurn},
url = {http://eprints.soton.ac.uk/357187/},
year = {2013},
date = {2013-01-01},
booktitle = {5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys2013)},
abstract = {We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }

No data available.

http://eprints.soton.ac.uk/346637/

@inproceedings{eps346637,
title = {Cooperative energy exchange for the efficient use of energy and resources in remote communities. [Winner, Best Student Paper Award at AAMAS2013]},
author = {Muddasser Alam and Sarvapali Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/346637/},
year = {2013},
date = {2013-01-01},
booktitle = {Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings} }