Stratification and knowledge base management

Lassez, Catherine; McAloon, Ken; Port, Graeme S.

doi:10.1016/s0747-7171(89)80031-8

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In analytical stratification, each stratum is determined by a specific name (label) and all elements from this stratum have this label (name). Knowledge base stratification used for handling inconsistent knowledge bases [56,57], for constructing models of a knowledge base [58] and for merging multiple knowledge bases [59,60] is analytical. The same is logic stratification used for formalization of commonsense reasoning [61].…”

Section: Stratification Of Knowledge Systemsmentioning

confidence: 99%

“…Benferhat and Garcia [57] employ stratification for handling inconsistent knowledge bases. Lassez, et al, [58] show how stratification can be used as a tool in the interactive model-building process. Namely it is possible to reduce the computational complexity of the process by the use of stratification, which limits consistency checking to minimal strata.…”

Section: Short-term Memorymentioning

confidence: 99%

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Epistemic Information in Stratified M-Spaces

Burgin

2011

Information

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Information is usually related to knowledge. However, the recent development of information theory demonstrated that information is a much broader concept, being actually present in and virtually related to everything. As a result, many unknown types and kinds of information have been discovered. Nevertheless, information that acts on knowledge, bringing new and updating existing knowledge, is of primary importance to people. It is called epistemic information, which is studied in this paper based on the general theory of information and further developing its mathematical stratum. As a synthetic approach, which reveals the essence of information, organizing and encompassing all main directions in information theory, the general theory of information provides efficient means for such a study. Different types of information dynamics representation use tools of mathematical disciplines such as the theory of categories, functional analysis, mathematical logic and algebra. Here we employ algebraic structures for exploration of information and knowledge dynamics. In Introduction (Section 1), we discuss previous studies of epistemic information. Section 2 gives a compressed description of the parametric phenomenological definition of information in the general theory of information. In Section 3, anthropic information, which is received, exchanged, processed and used by people is singled out and studied based on the Componential Triune Brain model. One of the basic forms of anthropic information called epistemic information, which is related to knowledge, is analyzed in Section 4. Mathematical models of epistemic information are studied in Section 5. In Conclusion, some open problems related to epistemic information are given

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Section: Stratification Of Knowledge Systemsmentioning

confidence: 99%

Section: Short-term Memorymentioning

confidence: 99%

Epistemic Information in Stratified M-Spaces

Burgin

2011

Information

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“…By exploiting standard methods for testing whether a logic program is stratified (cf. [ 38 ] ), one can efficiently decide whether a given DDLP program P is N-acyclic in linear time in the size of P .…”

Section: Logic Programs With Double Negationmentioning

confidence: 99%

Semantic forgetting in answer set programming

Eiter

Wang

2008

Artificial Intelligence

102

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The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.

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“…In this way it is possible to prove that the operator T corresponding to the program is monotone. A different approach was taken by C. Lassez, K. McAloon, and G. Port [1987] in data base management. Their stratification is not necessarily linear; it is a quasi-ordering of predicates in so-called disjunctive databases.…”

Section: Ivan Soskov Maximal Concepts Of Computability On Abstract Smentioning

confidence: 99%

European Summer Meeting of the Association for Symbolic Logic

Thiele¹

1992

J. symb. log.

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Stratification and knowledge base management

Cited by 6 publications

References 12 publications

Epistemic Information in Stratified M-Spaces

Epistemic Information in Stratified M-Spaces

Semantic forgetting in answer set programming

European Summer Meeting of the Association for Symbolic Logic

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