Discovering State Invariants

13 years 10 months ago

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We continue to advocate a methodology that we used earlier for pattern discovery through exhaustive search in selected small domains. This time we apply it to the problem of discovering state invariants in planning domains. State invariants are formulas that if true in a state, will be true in all successor states. In this paper, we consider the following four types of state invariants commonly found in AI planning domains: functional dependency constraints, constraints on mutual exclusiveness of categories, type information constraints, and domain closure axioms. As it turned out, for a class of action theories that include many planning benchmarks, for the ﬁrst three types of constraints, whether they are state invariants can be veriﬁed by considering models whose domains are bounded by a small ﬁnite number. This forms the basis for a procedure that tries to discover state invariants by exhaustive search in small ﬁnite domains. An implementation of the procedure yields encou...

Fangzhen Lin

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