Planning and acting in partially observable stochastic domains
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Adaptive multi-agent programming in GTGolog
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Intention recognition in the situation calculus and probability theory frameworks
CLIMA'05 Proceedings of the 6th international conference on Computational Logic in Multi-Agent Systems
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We present the agent programming language POGTGolog, which combines explicit agent programming in Golog with game-theoretic multi-agent planning in a special kind of partially observable stochastic games (POSGs). The approach allows for partially specifying a high-level control program for a system of multiple agents, and for optimally filling in missing details by viewing it as a generalization of a special POSG and computing a Nash equilibrium.