Cooperation

Coalition Structure Generation Problems: optimization and parallelization of the IDP algorithm

Publication Type:

Conference Paper

Source:

OPTMAS 6th International Workshop @AAMAS on Optimization in Agent Systems (2013)

Trust Alignment: a Sine Qua Non of Open Multi-Agent Systems

Publication Type:

Conference Paper

Source:

On the Move to Meaningful Internet Systems: OTM 2011., Springer, Volume 7044, Hersonissos, Greece, p.182-199 (2011)

Abstract:

In open multi-agent systems trust is necessary to improve cooperation by enabling agents to choose good partners. Most trust models work by taking, in addition to direct experiences, other agents’ communicated evaluations into account. However, in an open multi-agent system other agents may use different trust models and as such the evaluations they communicate are based on different principles. This article shows that trust alignment is a crucial tool in this communication. Furthermore we show that trust alignment improves significantly if the description of
the evidence, upon which a trust evaluation is based, is taken into account.

Multi-Agent Coordination: DCOPs and Beyond

Publication Type:

Conference Paper

Source:

Twenty-Second International Joint Conference on Artificial Intelligence, AAAI Press, Volume 3, Barcelona, p.2838-2839 (2011)

ISBN:

978-1-57735-515-1

URL:

http://ijcai.org/papers11/contents.php

Abstract:

Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations.

Improving function filtering for computationally demanding DCOPs

Publication Type:

Conference Paper

Source:

Workshop on Distributed Constraint Reasoning at IJCAI 2011, Barcelona, p.99-111 (2011)

Abstract:

In this paper we focus on solving DCOPs in computationally demanding scenarios. GDL optimally solves DCOPs, but requires exponentially large cost functions, being impractical in such settings. Function filtering is a technique that reduces the size of cost functions. We improve the effectiveness of function filtering to reduce the amount of resources required to optimally solve DCOPs. As a result, we enlarge the range of problems solvable by algorithms employing function filtering.

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