Updating logical databases
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
Artificial Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Reasoning about partially observed actions
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On the progression of situation calculus basic action theories: resolving a 10-year-old conjecture
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Decidable reasoning in a modified situation calculus
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Artificial Intelligence
A classification of first-order progressable action theories in situation calculus
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Logical filtering is the process of updating a belief state (set of possible world states) after a sequence of executed actions and perceived observations. In general, it is intractable in dynamic domains that include many objects and relationships. Still, potential applications for such domains (e.g., semantic web, autonomous agents, and partial-knowledge games) encourage research beyond immediate intractability results. In this paper we present polynomial-time algorithms for filtering belief states that are encoded as First-Order Logic (FOL) formulae. We sidestep previous discouraging results, and show that our algorithms are exact in many cases of interest. These algorithms accept belief states in full FOL, which allows natural representation with explicit references to unidentified objects, and partially known relationships. Our algorithms keep the encoding compact for important classes of actions, such as STRIPS actions. These results apply to most expressive modeling languages, such as partial databases and belief revision in FOL.