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
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
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
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
Reasoning about actions in a probabilistic setting
Eighteenth national conference on Artificial intelligence
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
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
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Probabilistic reasoning about actions in nonmonotonic causal theories
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Distributed AI for ambient intelligence: issues and approaches
AmI'07 Proceedings of the 2007 European conference on Ambient intelligence
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We present the action language for reasoning about actions in multi-agent systems under probabilistic uncertainty and partial observability, which is an extension of the action language that is inspired by partially observable stochastic games (POSGs). We provide a finite-horizon value iteration for this framework and show that it characterizes finite-horizon Nash equilibria. We also describe how the framework can be implemented on top of nonmonotonic causal theories. We then present acyclic action descriptions in as a special case where transitions are computable in polynomial time. We also give an example that shows the usefulness of our approach in practice.