Discovering State Invariants

@inproceedings{Lin2004DiscoveringSI,
  title={Discovering State Invariants},
  author={Fangzhen Lin},
  booktitle={KR},
  year={2004}
}
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… CONTINUE READING

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