The computational complexity of propositional STRIPS planning
Artificial Intelligence
Artificial Intelligence
Coordinating Mutually Exclusive Resources using GPGP
Autonomous Agents and Multi-Agent Systems
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Multi-Agent Planning as Search for a Consensus that Maximizes Social Welfare
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Approximation Properties of Planning Benchmarks
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Computationally feasible VCG mechanisms
Journal of Artificial Intelligence Research
Coordination through plan repair
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Creating Incentives to Prevent Intentional Execution Failures
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Introduction to planning in multiagent systems
Multiagent and Grid Systems - Planning in multiagent systems
Towards cooperation in adversarial search with confidentiality
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Multiagent task allocation in social networks
Autonomous Agents and Multi-Agent Systems
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
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Multiagent planning methods are concerned with planning by and for a group of agents. If the agents are self-interested, they may be tempted to lie in order to obtain an outcome that is more rewarding for them. We therefore study the multiagent planning problem from a mechanism design perspective, showing how to incentivise agents to be truthful. We prove that the well-known truthful VCG mechanism is not always truthful in the context of optimal planning, and present a modification to fix this. Finally, we present some (domain-dependent) poly-time planning algorithms using this fix that maintain truthfulness in spite of their non-optimality.