The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Probabilistic planning via heuristic forward search and weighted model counting
Journal of Artificial Intelligence Research
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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In conformant probabilistic planning (CPP), we are given a set of actions with stochastic effects, a distribution over initial states, a goal condition, and a value 0 p ≤ 1. Our task is to find a plan π such that the probability that the goal condition holds following the execution of π in the initial state is at least p. In this paper we focus on the problem of CPP with deterministic actions. Motivated by the success of the translation-based approach of Palacious and Geffner [6], we show how deterministic CPP can be reduced to a metric-planning problem. Given a CPP, our planner generates a metric planning problem that contains additional variables. These variables represent the probability of certain facts. Standard actions are modified to update these values so that this semantics of the value of variables is maintained. An empirical evaluation of our planner, comparing it to the best current CPP solver, Probabilistic-FF, shows that it is a promising approach.