Conformant Planning via Model Checking
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Complete Classes of Strategies for the Classical Iterated Prisoner's Dilemma
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Conformant planning for domains with constraints: a new approach
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A formalization of equilibria for multiagent planning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real-time strategy gaines: a new AI research challenge
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
The general game playing description language is universal
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Generating project plans for data center transformations
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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When several agents operate in a common environment, their plans may interfere so that the predicted outcome of each plan may be altered, even if it is composed of deterministic actions, only. Most of the multi-agent planning frameworks either view the actions of the other agents as exogeneous events or consider goal sharing cooperative agents. In this paper, we depart from such frameworks and extend the well-known single agent framework for classical planning to a multi-agent one. Focusing on the two agents case, we show how valuable plans can be characterized using game-theoretic notions, especially Nash equilibrium.