Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).

http://eprints.soton.ac.uk/267288/

@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1--41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

We propose a novel variant of the Continuous Double Auction (CDA), the Trust-based CDA (T-CDA), which we demonstrate to be robust to execution uncertainty. This is desirable in a setting where traders may fail to deliver the goods, services or payments they have promised. Specifically, the TCDA provides a mechanism that allows agents to commit to trades they believe will maximize their expected utility. In this paper, we consider agents that use their trust in other agents to estimate the expected utility of a transaction. We empirically evaluate the mechanism, both against the optimal solution given perfect and complete information and against the standard CDA.We show that the T-CDA consistently outperforms the traditional CDA as execution uncertainty increases in the system. Furthermore, we investigate the robustness of the mechanism to unreliable trust information and find that performance degrades gracefully as information quality decreases.

http://eprints.soton.ac.uk/267329/

@article{eps267288,
title = {Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty},
author = {Sarvapali D. Ramchurn and Claudio Mezzetti and Andrea Giovannucci and Juan A. Rodriguez and Rajdeep K. Dash and Nicholas R. Jennings},
url = {http://eprints.soton.ac.uk/267288/},
year = {2009},
date = {2009-01-01},
journal = {Journal of Artificial Intelligence Research},
volume = {35},
pages = {1--41},
abstract = {Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent's probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2x10^ 5 possible allocations in 40 seconds).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains.

http://eprints.soton.ac.uk/272011/

@inproceedings{eps272011,
title = {Intelligent Agents for Disaster Management},
author = {Niall Adams and Martin Field and Erol Gelenbe and David Hand and Nicholas Jennings and David Leslie and David Nicholson and Sarvapali Ramchurn and Alex Rogers},
url = {http://eprints.soton.ac.uk/272011/},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the IARP/EURON Workshop on Robotics for Risky Interventions and Environmental Surveillance (RISE)},
abstract = {ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England.

http://eprints.soton.ac.uk/265122/

@inproceedings{eps265122,
title = {Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes},
author = {Michael A Osborne and Alex Rogers and Sarvapali Ramchurn and Stephen J Roberts and N. R. Jennings},
url = {http://eprints.soton.ac.uk/265122/},
year = {2008},
date = {2008-01-01},
booktitle = {International Conference on Information Processing in Sensor Networks (IPSN 2008)},
pages = {109--120},
abstract = {In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England.}, note = {Event Dates: April 2008},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental parameters will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and the correlations and delays that exist between them. We validate our approach using data collected from a network of weather sensors located on the south coast of England.

http://eprints.soton.ac.uk/264967/

@inproceedings{eps264967,
title = {Information Agents for Pervasive Sensor Networks},
author = {Alex Rogers and Michael A Osborne and Sarvapali Ramchurn and Stephen J Roberts and N. R. Jennings},
url = {http://eprints.soton.ac.uk/264967/},
year = {2008},
date = {2008-01-01},
booktitle = {Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2008)},
pages = {294--299},
abstract = {In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental parameters will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and the correlations and delays that exist between them. We validate our approach using data collected from a network of weather sensors located on the south coast of England.},
note = {Event Dates: March 2008},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

No data available.

http://eprints.soton.ac.uk/265985/

@incollection{eps265985,
title = {Intrusiveness Management for Focused, Efficient, and Enjoyable Activities},
author = {Fredrik Espinoza, and David De Roure and Ola Hamfors and Lucas Hinz and Jesper Holmberg and Carl-Gustaf Jansson and Nick Jennings and Mike Luck and Peter L"onnqvist and Sarvapali Ramchurn and Anna Sandin and Mark Thompson and Markus Bylund},
url = {http://eprints.soton.ac.uk/265985/},
year = {2007},
date = {2007-01-01},
booktitle = {The Disappearing Computer: Interaction Design, System Infrastructures and Applications for Smart Environments},
pages = {143--160},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}

Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best set of agents that should participate in a given team. To this end, in this paper, we present a novel, anytime algorithms designed for this purpose. Our algorithm can generate solutions that either have a tight bound from the optimal or are optimal (depending on the objective) and works by partitioning the space in terms of a small set of elements that represent structures which contain coalitions of particular sizes. It then performs an online heuristic search that prunes the space and only considers valid and non-redundant coalition structures. We empirically show that we are able to find solutions that are, in the worst case, 99% efficient in 0.0043% of the time to find the optimal value by the state of the art dynamic programming (DP) algorithm (for 20 agents), using 33% less memory.

http://eprints.soton.ac.uk/263074/

@inproceedings{eps263074,
title = {Near-optimal anytime coalition structure generation},
author = {T. Rahwan and S.D. Ramchurn and V.D. Dang and N. R. Jennings},
url = {http://eprints.soton.ac.uk/263074/},
year = {2007},
date = {2007-01-01},
booktitle = {20th International Joint Conference on Artificial Intelligence (IJCAI)},
pages = {2365--2371},
abstract = {Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best set of agents that should participate in a given team. To this end, in this paper, we present a novel, anytime algorithms designed for this purpose. Our algorithm can generate solutions that either have a tight bound from the optimal or are optimal (depending on the objective) and works by partitioning the space in terms of a small set of elements that represent structures which contain coalitions of particular sizes. It then performs an online heuristic search that prunes the space and only considers valid and non-redundant coalition structures. We empirically show that we are able to find solutions that are, in the worst case, 99% efficient in 0.0043% of the time to find the optimal value by the state of the art dynamic programming (DP) algorithm (for 20 agents), using 33% less memory.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best groups of agents to select to achieve some goal. To this end, in this paper, we present a novel, optimal anytime algorithm for this coalition structure generation problem that is significantly faster than previous algorithms designed for this purpose. Specifically, our algorithm can generate solutions by partitioning the space of all potential coalitions into sub-spaces that contain coalition structures that are similar, according to some criterion, such that these sub-spaces can be pruned by identifying their bounds. Using this representation, the algorithm then searches through only valid and unique coalition structures and selects the best among them using a branch-and-bound technique. We empirically show that we are able to find solutions that are optimal in 0.082% of the time taken by the state of the art dynamic programming algorithm (for 27 agents) using much less memory (O(2^ n) instead of O(3^ n) for the set of n agents). Moreover, our algorithm is the first to be able to solve the coalition structure generation problem for numbers of agents bigger than 27 in reasonable time (less than 90 minutes for 27 agents as opposed to around 2 months for the best previous solution).

http://eprints.soton.ac.uk/263433/

@inproceedings{eps263433,
title = {Anytime Optimal Coalition Structure Generation},
author = {Talal Rahwan and Sarvapali D. Ramchurn and Viet D. Dang and Andrea Giovannucci and N. R. Jennings},
url = {http://eprints.soton.ac.uk/263433/},
year = {2007},
date = {2007-01-01},
booktitle = {22nd Conference on Artificial Intelligence (AAAI)},
pages = {1184--1190},
abstract = {Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best groups of agents to select to achieve some goal. To this end, in this paper, we present a novel, optimal anytime algorithm for this coalition structure generation problem that is significantly faster than previous algorithms designed for this purpose. Specifically, our algorithm can generate solutions by partitioning the space of all potential coalitions into sub-spaces that contain coalition structures that are similar, according to some criterion, such that these sub-spaces can be pruned by identifying their bounds. Using this representation, the algorithm then searches through only valid and unique coalition structures and selects the best among them using a branch-and-bound technique. We empirically show that we are able to find solutions that are optimal in 0.082% of the time taken by the state of the art dynamic programming algorithm (for 27 agents) using much less memory (O(2^ n) instead of O(3^ n) for the set of n agents). Moreover, our algorithm is the first to be able to solve the coalition structure generation problem for numbers of agents bigger than 27 in reasonable time (less than 90 minutes for 27 agents as opposed to around 2 months for the best previous solution).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}