Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Readings in uncertain reasoning
Readings in uncertain reasoning
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Modular utility representation for decision-theoretic planning
Proceedings of the first international conference on Artificial intelligence planning systems
Commitment strategies in planning: a comparative analysis
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Anytime synthetic projection: maximizing the probability of goal satisfaction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
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We introduce an approach to high-level conditional planning we call ε-safe planning. This probabilistic approach commits us to planning to meet some specified goal witch a probability of success of at least 1 - ε for some user-supplied ε. We describe several algorithms for ε-safe planning based on conditional planners. The two conditional planners we discuss are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear conditional planner, PLINTH. We present a straightforward extension to conditional planners for which computing the necessary probabilities is simple, employing a commonly-made but perhaps overly-strong independence assumption. We also discuss a second approach to ε-safe planning which relaxes this independence assumption, involving the incremental construction of a probability dependence model in conjunction with the construction of the plan graph.