SATCHMORE: SATCHMO with RElevancy

Loveland, Donald W.; Reed, David; Wilson, Debra Sue

doi:10.1007/bf00881861

Cited by 37 publications

(6 citation statements)

References 11 publications

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“…In contrast to our algorithm, the above methods 38,53,40,39] are (completely or partially) based on a top-down computational model. Thus, our algorithm has the well-known advantages and drawbacks of bottom-up evaluation strategies, while these methods have the advantages and drawbacks of top-down strategies.…”

Section: Related Workmentioning

confidence: 99%

Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation

Leone

Rullo

Scarcello

1997

Information and Computation

141

187

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Disjunctive logic programs have become a powerful tool in knowledge representation and commonsense reasoning. This paper focuses on stable model semantics, currently the most widely acknowledged semantics for disjunctive logic programs. After presenting a new notion of unfounded sets for disjunctive logic programs, we provide two declarative characterizations of stable models in terms of unfounded sets. One shows that the set of stable models coincides with the family of unfounded-free models (i.e., a model is stable i it contains no unfounded atoms). The other proves that stable models can be de ned equivalently by a property of their false literals, as a model is stable i the set of its false literals coincides with its greatest unfounded set. We then generalize the well-founded W P operator to disjunctive logic programs, give a xpoint semantics for disjunctive stable models and present an algorithm for computing the stable models of function-free programs. The algorithm's soundness and completeness are proved and some complexity issues are discussed.

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Section: Related Workmentioning

confidence: 99%

Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation

Leone

Rullo

Scarcello

1997

Information and Computation

141

187

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“…This is shown in detail in Reference. 16) The following example taken from Reference 16) demonstrates the possible gains resulting from relevancy testing employed by SATCHMORE.…”

Section: Example 21mentioning

confidence: 98%

“…Relevance testing is recognized as an approach to limit the number of clauses that participate in a refutation. 16'~9'~~ Partial relevancy TM and total relevancy ~6) were introduced as means to restrict the processing to those clauses that are known to contribute to the refutation. Partial relevancy relies on having at least one atom of the head of a clause relevant before that clause can be used in a SATCHMO-style refutation while total relevancy requires all head atoms be relevant for this purpose.…”

Section: W Introductionmentioning

confidence: 99%

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

Loveland

Yahya

2003

New Gener Comput

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SATCHMORE was introduced as a mechanism to integrate relevancy testing with the model-generation theorem prover SATCHMO. This made it possible to avoid invoking some clauses that appear in no refutation, which was a major drawback of the SATCHMO approach. SATCHMORE relevancy, however, is driven by the entire set of negative clauses and no distinction is accorded to the query negation. Under unfavorable circumstances, such as in the presence of large amounts of negative data, this can reduce the efficiency of SATCHMORE. In this paper we introduce a further refinement of SATCHMO called SATCHMOREBID: SATCHMORE with BIDirectional relevancy. SATCHMOREBID uses only the negation of the query for relevancy determination at the start. Other negative clauses are introduced on demand and only if a refutation is not possible using the current set of negative clauses. The search for the relevant negative clauses is performed in a forward chaining mode as opposed to relevancy propagation in SATCH-MORE which is based on backward chaining. SATCHMOREBID is shown to be refutation'ally sound and complete. Experiments on a prototype SATCH-MOREBID implementation point to its potential to enhance the efficiency of the query answering process in disjunctive databases.

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“…For the latter problem, we implemented two sorts of factorization mechanisms: static lemma generation (SLG) and dynamic lemma generation (DLG). Then, we came up with a new method that combines the relevancy testing realized by NHM and SATCHMORE [16] with folding-up (FU) proposed by Letz [15]. We call this method relevancy testing while folding-up (RFU).…”

Section: Research On Mgtpmentioning

confidence: 99%

MGTP: A model generation theorem prover — Its advanced features and applications —

Hasegawa

Fujita

Koshimura

1997

Lecture Notes in Computer Science

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This paper outlines a parallel model-generation based theoremproving system MGTP that we have been developing, focusing on the recent developments including novel techniques for efficient proof-search and successful applications. We have developed CMGTP (Constraint MGTP) to deal with constraint satisfaction problems. By attaining the constraint propagation with negative atoms, CMGTP makes it possible to reduce search spaces by orders of magnitude compared to the original MGTP. To enhance the ability to prune search spaces, we have developed a new method called non-Horn magic sets (NHM) and incorporated its relevancy testing function into the folding-up (FU) method proposed by Letz. The NHM method suppresses useless model generation with clauses irrelevant to the goal. The FU method avoids generating duplicated subproofs after case-splitting. With these methods we can eliminate two major kinds of redundancies in model-generation based theorem provers. We have studied several applications in AI such as negation as failure, abductive reasoning and modal logic systems, through extensive use of MGTP. These studies share a basic common idea, that is, to use MGTP as a meta-programming system. We can build various reasoning systems on MGTP by writing the specific inference rules for each system in MGTP input clauses. Furthermore, we are now working on other applications such as machine learning with MGTP and heuristic proof-search based on the genetic algorithm.

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SATCHMORE: SATCHMO with RElevancy

Cited by 37 publications

References 11 publications

Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation

Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

MGTP: A model generation theorem prover — Its advanced features and applications —

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