Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
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
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Combining probabilities, failures and safety in robot control
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Team programming in Golog under partial observability
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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We present the agent programming language POGTGolog, which integrates explicit agent programming in Golog with game-theoretic multi-agent planning in partially observable stochastic games. It deals with the case of one team of cooperative agents under partial observability, where the agents may have different initial belief states and not necessarily the same rewards. POGTGolog allows for specifying a partial control program in a high-level logical language, which is then completed by an interpreter in an optimal way. To this end, we define a formal semantics of POGTGolog programs in terms of Nash equilibria, and we specify a POGTGolog interpreter that computes one of these Nash equilibria. We illustrate the usefulness of POGTGolog along a rugby scenario.