Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
Representing plans under uncertainty: a logic of time, chance, and action
Representing plans under uncertainty: a logic of time, chance, and action
Reasoning about time and probability
Reasoning about time and probability
PRODIGY 4.0: The Manual and Tutorial
PRODIGY 4.0: The Manual and Tutorial
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
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Human-aware planning for robots embedded in ambient ecologies
Pervasive and Mobile Computing
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I describe a planning methodology for domains with uncertainty in the form of external events that are not completely predictable. The events are represented by enabling conditions and probabilities of occurrence. The planner is goal-directed and backward chaining, but the subgoals are suggested by analysing the probability of success of the partial plan rather than being simply the open conditions of the operators in the plan. The partial plan is represented as a Bayesian belief net to compute its probability of success. Since calculating the probability of success of a plan can be very expensive I introduce two other techniques for computing it, one that uses Monte Carlo simulation to estimate it and one based on a Markov chain representation that uses knowledge about the dependencies between the predicates describing the domain.