Updating logical databases
Artificial intelligence and mathematical theory of computation
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Artificial Intelligence
Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Bounded Model Checking Using Satisfiability Solving
Formal Methods in System Design
Combining strengths of circuit-based and CNF-based algorithms for a high-performance SAT solver
Proceedings of the 39th annual Design Automation Conference
Conformant planning via symbolic model checking
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Regression for Classical and Nondeterministic Planning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Stochastic filtering in a probabilistic action model
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Combining planning and motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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Logical Filtering is the problem of tracking the possible states of a world (belief state) after a sequence of actions and observations. It is fundamental to applications in partially observable dynamic domains. This paper presents the first exact logical filtering algorithm that is tractable for all deterministic domains. Our tractability result is interesting because it contrasts sharply with intractability results for structured stochastic domains. The key to this advance lies in using logical circuits to represent belief states. We prove that both filtering time and representation size are linear in the sequence length and the input size. They are independent of the domain size if the actions have compact representations. The number of variables in the resulting formula is at most the number of state features. We also report on a reasoning algorithm (answering propositional questions) for our circuits, which can handle questions about past time steps (smoothing). We evaluate our algorithms extensively on AI planning domains. Our method outperforms competing methods, sometimes by orders of magnitude.