Ramchurn, Sarvapali D., Mezzetti, Claudio, Giovannucci, Andrea, Rodriguez-Aguilar, Juan A., Dash, Rajdeep K. and Jennings, Nicholas R.. (2009) Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty. Journal of Artificial Intelligence Research, Vol.35 . pp. 119-159. ISSN 1076-9757

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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 2 x 105 possible allocations in 40 seconds).

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Social Sciences > Economics
Journal or Publication Title:	Journal of Artificial Intelligence Research
Publisher:	A A A I Press
ISSN:	1076-9757
Date:	2009
Volume:	Vol.35
Number of Pages:	41
Page Range:	pp. 119-159
Identification Number:	10.1613/jair.2751
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	IEA, Agreement Technologies, Jose Castillejo programme of the Spanish Ministry of Science and Innovation, Juan De La Cierva Contract, EU, Fondazione Cassa di Risparmio di Padova e Rovigo, BAE Systems, EPSRC
Grant number:	TIN2006-15662-C02-01, CONSOLIDER CSD2007-0022, INGENIO 2010, JC2008-00337, JCI-2008-03006, ICT-217148-SF, EP/C548051/1
URI:	http://wrap.warwick.ac.uk/id/eprint/27753

Data sourced from Thomson Reuters' Web of Knowledge

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