A model for reasoning about persistence and causation
Computational Intelligence
Updating logical databases
Artificial intelligence and mathematical theory of computation
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
Proving properties of states in the situation calculus
Artificial Intelligence
Artificial Intelligence
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Interpolation Theorems for Resolution in Lower Predicate Calculus
Journal of the ACM (JACM)
The size of a revised knowledge base
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Bounded Model Checking Using Satisfiability Solving
Formal Methods in System Design
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
JELIA '00 Proceedings of the European Workshop on Logics in Artificial Intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Background, Reserve, and Gandy Machines
Proceedings of the 14th Annual Conference of the EACSL on Computer Science Logic
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
FLUX: A logic programming method for reasoning agents
Theory and Practice of Logic Programming
Scaling up reasoning about actions using relational database technology
AAAI'04 Proceedings of the 19th national conference on Artifical 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
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
The complexity of belief update
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Completeness theorems for semantic resolution in consequence-finding
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
Progression of situation calculus action theories with incomplete information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Practical partition-based theorem proving for large knowledge bases
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A unified view of consequence relation, belief revision and conditional logic
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
How to progress a database II: the STRIPS connection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Partition-based logical reasoning for first-order and propositional theories
Artificial Intelligence - Special volume on reformulation
What is believed is what is explained (sometimes)
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Artificial Intelligence
Propositional Update Operators Based on Formula/Literal Dependence
ACM Transactions on Computational Logic (TOCL)
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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 intractability results. In this paper we present polynomial-time algorithms for filtering belief states that are encoded as First-Order Logic (FOL) formulas. Our algorithms are exact in many cases of interest. They accept belief states in FOL without functions, permitting arbitrary arity for predicates, infinite universes of elements, and equality. They enable natural representation with explicit references to unidentified objects and partially known relationships, still maintaining tractable computation. Previous results focus on more general cases that are intractable or permit only imprecise filtering. Our algorithms guarantee that belief-state representation remains compact for STRIPS actions (among others) with unbounded-size domains. This guarantees tractable exact filtering indefinitely for those domains. The rest of our results apply to expressive modeling languages, such as partial databases and belief revision in FOL.