In this paper we present polynomial-time algorithms that translate First-Order Logic (FOL) theories to smaller propo-sitional encodings than achievable before in polynomial time. For example, we can sometimes reduce the number of propo-sitions to O(|P | + |C|), or O(|P |k · log |P |), for |P | predi-cates of arity k and |C | constant symbols. The guarantee de-pends on availability of some graphical structure in the FOL representation. Our algorithms accept all FOL theories, and preserve soundness and completeness (sometimes requiring the Domain Closure Assumption). Our experiments show significant speedup in inference with a SAT solver on real-world problems. Our results address a common approach that translates inference and decision probl...
This dissertation studies the fine-grained complexity of model checking problems for fixed logical f...
We establish a framework to integrate propositional logic with firstorder logic. This is done in su...
Knowledge Representation and Reasoning is the area of artificial intelligence that is concerned with...
This paper presents an effective method to encode function-free first-order Horn theories in proposi...
AbstractWe present a method for encoding first order proofs in SMT. Our implementation, called ChewT...
In this paper we provide algorithms for reasoning with partitions of related logical axioms in propo...
Recent improvements in satisfiability algorithms for propositional logic have made partial instantia...
Solving various combinatorial problems by their translation to the propositional satisfiability prob...
We study and implement algorithms to revise and learn first-order logical theories, written in claus...
We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Int...
: We have argued elsewhere that first order inference can be made more efficient by using non-standa...
Abstract. We present a method for proving rigid first order theorems by encoding them as proposition...
We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Int...
Human beings often have to reason and make decisions based on uncertain knowledge of real world prob...
We connect learning algorithms and algorithms automating proof search in propositional proof systems...
This dissertation studies the fine-grained complexity of model checking problems for fixed logical f...
We establish a framework to integrate propositional logic with firstorder logic. This is done in su...
Knowledge Representation and Reasoning is the area of artificial intelligence that is concerned with...
This paper presents an effective method to encode function-free first-order Horn theories in proposi...
AbstractWe present a method for encoding first order proofs in SMT. Our implementation, called ChewT...
In this paper we provide algorithms for reasoning with partitions of related logical axioms in propo...
Recent improvements in satisfiability algorithms for propositional logic have made partial instantia...
Solving various combinatorial problems by their translation to the propositional satisfiability prob...
We study and implement algorithms to revise and learn first-order logical theories, written in claus...
We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Int...
: We have argued elsewhere that first order inference can be made more efficient by using non-standa...
Abstract. We present a method for proving rigid first order theorems by encoding them as proposition...
We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Int...
Human beings often have to reason and make decisions based on uncertain knowledge of real world prob...
We connect learning algorithms and algorithms automating proof search in propositional proof systems...
This dissertation studies the fine-grained complexity of model checking problems for fixed logical f...
We establish a framework to integrate propositional logic with firstorder logic. This is done in su...
Knowledge Representation and Reasoning is the area of artificial intelligence that is concerned with...