Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Winner determination in combinatorial auction generalizations
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences
Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
A Market Protocol for Decentralized Task Allocation
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The Knowledge Engineering Review
Graphs and Hypergraphs
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Bid expressiveness and clearing algorithms in multiattribute double auctions
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Minimum payments that reward honest reputation feedback
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Computational-Mechanism Design: A Call to Arms
IEEE Intelligent Systems
A comparison between mechanisms for sequential compute resource auctions
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Fault tolerant mechanism design
Artificial Intelligence
Computationally feasible VCG mechanisms
Journal of Artificial Intelligence Research
Obtaining reliable feedback for sanctioning reputation mechanisms
Journal of Artificial Intelligence Research
Bidding languages and winner determination for mixed multi-unit combinatorial auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Achieving budget-balance with Vickrey-based payment schemes in exchanges
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Scalable mechanism design for the procurement of services with uncertain durations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Auctions and bidding: A guide for computer scientists
ACM Computing Surveys (CSUR)
Update of Probabilistic Beliefs: Implementation and Parametric Verification
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Algorithms and mechanisms for procuring services with uncertain durations using redundancy
Artificial Intelligence
Strategy-Proof mechanisms for interdependent task allocation with private durations
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
A rank-and-compare algorithm to detect abnormally low bids in procurement auctions
Electronic Commerce Research and Applications
Merging multiple information sources in federated sponsored search auctions
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi criteria operators for multi-attribute auctions
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
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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 2×105 possible allocations in 40 seconds).