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Ryuzo Hasegawa - One of the best experts on this subject based on the ideXlab platform.
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LPAR - Abstract Model Generation for Preprocessing Clause Sets
Logic for Programming Artificial Intelligence and Reasoning, 2005Co-Authors: Miyuki Koshimura, Mayumi Umeda, Ryuzo HasegawaAbstract:Abstract Model Generation refers to Model Generation for abstract clause sets in which arguments of atoms are ignored. We give two abstract clause sets which are obtained from normal clause sets. One is for checking satisfiability of the original normal clause set. Another is used for eliminating unnecessary clauses from the original one. These abstract clause sets are propositional, i.e. decidable. Thus, we can use them for preprocessing the original one.
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Model Generation Theorem Proving with Finite Interval Constraints
2003Co-Authors: Reiner Hähnle, Ryuzo Hasegawa, Yasuyuki ShiraiAbstract:Model Generation Theorem Proving (MGTP) is a class of deduction procedures for first-order logic that were successfully used to solve hard combinatorial problems. For some applications the representation of Models in MGTP and its extension CMGTP causes redundancy. Here we suggest to extend members of Model candidates in such a way that a predicate p can have not only terms as arguments, but at certain places also subsets of totally ordered finite domains. The ensuing language and deduction system relies on constraints based on finite intervals in totally ordered sets and is called IV-MGTP. We show soundness/completeness of the procedure, and the experimental results that show considerable potential of the method.
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LPAR - Model Generation with Boolean Constraints
Logic for Programming Artificial Intelligence and Reasoning, 2001Co-Authors: Miyuki Koshimura, Hiroshi Fujita, Ryuzo HasegawaAbstract:We present a simple method for eliminating redundant searches in Model Generation. The method employs Boolean Constraints which are conjunctions of ground instances of clauses having participated in proofs. Boolean Constraints work as sets of lemmas with which duplicate subproofs and irrelevant Model extensions can be eliminated. The method has been tentatively implemented on a constraint logic programming system. We evaluated effects of the method by proving some typical problems taken from the CASC-JS system competition.
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CADE - Efficient Minimal Model Generation Using Branching Lemmas
Automated Deduction - CADE-17, 2000Co-Authors: Ryuzo Hasegawa, Hiroshi Fujita, Miyuki KoshimuraAbstract:An efficient method for minimal Model Generation is presented. The method employs branching assumptions and lemmas so as to prune branches that lead to nonminimal Models, and to reduce minimality tests on obtained Models. This method is applicable to other approaches such as Bry’s complement splitting and constrained search or Niemela’s groundedness test, and greatly improves their efficiency. We implemented MM-MGTP based on the method. Experimental results with MM-MGTP show a remarkable speedup compared to MM-SATCHMO.
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TABLEAUX - MGTP: A Model Generation Theorem Prover - Its Advanced Features and Applications
Lecture Notes in Computer Science, 1997Co-Authors: Ryuzo Hasegawa, Hiroshi Fujita, Miyuki KoshimuraAbstract:This paper outlines a parallel Model-Generation based theorem-proving system MGTP that we have been developing, focusing on the recent developments including novel techniques for efficient proof-search and successful applications.
Miyuki Koshimura - One of the best experts on this subject based on the ideXlab platform.
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LPAR - Abstract Model Generation for Preprocessing Clause Sets
Logic for Programming Artificial Intelligence and Reasoning, 2005Co-Authors: Miyuki Koshimura, Mayumi Umeda, Ryuzo HasegawaAbstract:Abstract Model Generation refers to Model Generation for abstract clause sets in which arguments of atoms are ignored. We give two abstract clause sets which are obtained from normal clause sets. One is for checking satisfiability of the original normal clause set. Another is used for eliminating unnecessary clauses from the original one. These abstract clause sets are propositional, i.e. decidable. Thus, we can use them for preprocessing the original one.
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LPAR - Model Generation with Boolean Constraints
Logic for Programming Artificial Intelligence and Reasoning, 2001Co-Authors: Miyuki Koshimura, Hiroshi Fujita, Ryuzo HasegawaAbstract:We present a simple method for eliminating redundant searches in Model Generation. The method employs Boolean Constraints which are conjunctions of ground instances of clauses having participated in proofs. Boolean Constraints work as sets of lemmas with which duplicate subproofs and irrelevant Model extensions can be eliminated. The method has been tentatively implemented on a constraint logic programming system. We evaluated effects of the method by proving some typical problems taken from the CASC-JS system competition.
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CADE - Efficient Minimal Model Generation Using Branching Lemmas
Automated Deduction - CADE-17, 2000Co-Authors: Ryuzo Hasegawa, Hiroshi Fujita, Miyuki KoshimuraAbstract:An efficient method for minimal Model Generation is presented. The method employs branching assumptions and lemmas so as to prune branches that lead to nonminimal Models, and to reduce minimality tests on obtained Models. This method is applicable to other approaches such as Bry’s complement splitting and constrained search or Niemela’s groundedness test, and greatly improves their efficiency. We implemented MM-MGTP based on the method. Experimental results with MM-MGTP show a remarkable speedup compared to MM-SATCHMO.
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TABLEAUX - MGTP: A Model Generation Theorem Prover - Its Advanced Features and Applications
Lecture Notes in Computer Science, 1997Co-Authors: Ryuzo Hasegawa, Hiroshi Fujita, Miyuki KoshimuraAbstract:This paper outlines a parallel Model-Generation based theorem-proving system MGTP that we have been developing, focusing on the recent developments including novel techniques for efficient proof-search and successful applications.
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embedding negation as failure into a Model Generation theorem prover
Conference on Automated Deduction, 1992Co-Authors: Katsumi Inoue, Miyuki Koshimura, Ryuzo HasegawaAbstract:Here, for the first time, we give an implementation which computes answer sets of every class of (function-free) logic programs and deductive databases containing both negation as failure and classical negation. The proposal is based on bottom-up, incremental, backtrack-free computation of the minimal Models of positive disjunctive programs, together with integrity constraints over beliefs and disbeliefs. Our translation method not only provides a simple fixpoint characterization of answer sets, but also is very helpful to understand under what conditions each Model is “stable” or “unstable”. The procedure has been implemented on top of the Model Generation theorem prover MGTP on a parallel inference machine, and has been applied to a legal reasoning system.
Karsten Konrad - One of the best experts on this subject based on the ideXlab platform.
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Model Generation for natural language interpretation and analysis
2004Co-Authors: Karsten KonradAbstract:Model Generation refers to the automatic Generation of mathematical structures that prove the satisfiability of logical theories. The research documented in this thesis investigates the use of Model Generation in the analysis and interpretation of formal semantic representations of natural language. Based on standard techniques for first-order Model Generation, we develop a Model Generation technique for a restricted higher-order logic and show how this method can be used to investigate the criteria that distinguish valid natural-language interpretations from interpretations that do not correspond to the intended meaning of the represented sentences. In particular, we investigate the analysis of singular definite descriptions and reciprocal sentences and show that Model Generation gives a computational method for describing theories of prefernece for natural-language interpretations. nicht vorhanden
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3 Higher-Order Model Generation
Lecture Notes in Computer Science, 2004Co-Authors: Karsten KonradAbstract:The simply typed λ-calculus can be taken as the basis of a formalism in which we assign meaning to the basic expressions of a natural language and explain the meaning of larger constituents by the composition of the meaning of their parts. However, under a conventional higher-order semantic for the λ-calculus, we cannot give a Model Generation method based on the well-known techniques of first-order Model Generation. By weakening the semantic of higher-order logic, we formulate a logical language that has the compositional expressivity of a higher-order logic but the finitely representable Models of a first-order one. A generic Model Generation technique for this language is presented and discussed, and a refinement for minimal Model Generation is introduced.
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2 Model Generation
Lecture Notes in Computer Science, 2004Co-Authors: Karsten KonradAbstract:This chapter presents research topics in Model Generation that will be relevant for later chapters. It also gives an introduction to some of the most popular methods for generating Models.
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4 Minimal Model Generation
Lecture Notes in Computer Science, 2004Co-Authors: Karsten KonradAbstract:We now define a new class of Models for \({\cal {MQL}}\) specification, the locally minimal Models. They are an amalgamation of domain minimal Models and subset-minimal Models known from first-order Model Generation. The property of being a local minimal Model in our logic \({\cal {MQL}}\) is decidable.
Martin Giese - One of the best experts on this subject based on the ideXlab platform.
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TABLEAUX - A Model Generation Style Completeness Proof for Constraint Tableaux with Superposition
Lecture Notes in Computer Science, 2002Co-Authors: Martin GieseAbstract:We present a calculus that integrates equality handling by superposition into a free variable tableau calculus. We prove completeness of this calculus by an adaptation of the Model Generation [2,15] technique commonly used for completeness proofs of resolution calculi. The calculi and the completeness proof are compared to earlier results of Degtyarev and Voronkov [7].
Katsumi Inoue - One of the best experts on this subject based on the ideXlab platform.
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IJCAI - Bottom-up abduction by Model Generation
1993Co-Authors: Katsumi Inoue, Ryuzo Hasegawa, Yoshihiko Ohta, Makoto NakashimaAbstract:We investigate two realizations of parallel abductive reasoning systems using the Model Generation theorem prover MGTP. The first one, called the MGTP + MGTP method, is a co-operative problem-solving architecture in which Model Generation and consistency checking communicate with each other. There, parallelism is exploited by checking consistencies in parallel. However, since this system consists of two different components, the possibilities for parallelization are limited. In contrast, the other method, called the Skip method, does not separate the inference engine from consistency checking, but realizes both functions in only one MGTP that is used as a generate-and-test mechanism. In this method, multiple Models can be kept in distributed memories, thus a great amount of parallelism can be obtained. We also attempt the upside-down meta-interpretation approach for abduction, in which top-down reasoning is simulated by a bottom-up reasoner.
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embedding negation as failure into a Model Generation theorem prover
Conference on Automated Deduction, 1992Co-Authors: Katsumi Inoue, Miyuki Koshimura, Ryuzo HasegawaAbstract:Here, for the first time, we give an implementation which computes answer sets of every class of (function-free) logic programs and deductive databases containing both negation as failure and classical negation. The proposal is based on bottom-up, incremental, backtrack-free computation of the minimal Models of positive disjunctive programs, together with integrity constraints over beliefs and disbeliefs. Our translation method not only provides a simple fixpoint characterization of answer sets, but also is very helpful to understand under what conditions each Model is “stable” or “unstable”. The procedure has been implemented on top of the Model Generation theorem prover MGTP on a parallel inference machine, and has been applied to a legal reasoning system.