Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
An algorithm for probabilistic least-commitment planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Optimal Probabilistic and Decision-Theoretic Planning using Markovian
Optimal Probabilistic and Decision-Theoretic Planning using Markovian
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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AI planning algorithms have addressed the problem of generating sequences of operators that achieve some input goal, usually assuming that the planning agent has perfect control over and information about the world. Relaxing these assumptions requires an extension to the action representation that allows reasoning both about the changes an action makes and the information it provides. This paper presents an action representation that extends the deterministic STRIPS model, allowing actions to have both causal and informational effects, both of which can be context dependent and noisy. We also demonstrate how a standard least-commitment planning algorithm can be extended to include informational actions and contingent execution.