Abstract
In this paper, we present a new approach to probabilistic planning based on logic programming, by relating probabilistic planning to hybrid probabilistic logic programs with probabilistic answer set semantics [32]. We show that any probabilistic planning problem, \(\cal P\), can be translated into a hybrid probabilistic logic program whose probabilistic answer sets correspond to trajectories in \(\cal P\), with associated probabilities. We formally prove the correctness of our approach. Moreover, we show that the complexity of finding a plan for a probabilistic planning problem in our approach is NP-complete. In addition, we show that any probabilistic planning problem, \(\cal P\), can be encoded as a classical logic program with answer set semantics, whose answer sets corresponds to valid trajectories in \(\cal P\). We also show that probabilistic planning problems can be encoded as proportional satisfiability problems.
Published Version
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