A Modified Semantics for LUPS

Leite, João

doi:10.1007/3-540-45329-6_27

Cited by 7 publications

(10 citation statements)

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“…In a concrete case study, we establish this for evolution frames which model policies in the EPI framework for logic program updates using the answer set semantics , as well as for the LUPS [Alferes et al 1999[Alferes et al , 2002 and LUPS * policies [Leite 2001] under the dynamic stable model semantics [Alferes et al 1998[Alferes et al , 2000. Similar results apply to updates under other semantics in the literature.…”

Section: Introductionmentioning

confidence: 90%

“…Examples of such frameworks are EPI , LUPS and LUPS * [Alferes et al 1999[Alferes et al , 2002Leite 2001], KABUL [Leite 2002], and, while not directly given in these terms, PDL [Lobo et al 1999].…”

Section: Capturing Framework For Knowledge Evolutionmentioning

confidence: 99%

“…, P n+1 ), where P n+1 is computed from the persistent commands PC n valid at state s, Bel(s), and the LUPS commands in A. Leite [2001] slightly modified and extended the semantics of LUPS by a permanent retraction command. The resulting framework, LUPS * , can be captured by the following evolution frame:…”

Section: Dynamic Logic Programs Lups and Lups *mentioning

confidence: 99%

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Reasoning about evolving nonmonotonic knowledge bases

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Fink

Sabbatini

et al. 2005

ACM Trans. Comput. Logic

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Recently, several approaches to updating knowledge bases modeled as extended logic programs have been introduced, ranging from basic methods to incorporate (sequences of) sets of rules into a logic program, to more elaborate methods which use an update policy for specifying how updates must be incorporated. In this article, we introduce a framework for reasoning about evolving knowledge bases, which are represented as extended logic programs and maintained by an update policy. We first describe a formal model which captures various update approaches, and we define a logical language for expressing properties of evolving knowledge bases. We then investigate semantical and computational properties of our framework, where we focus on properties of knowledge states with respect to the canonical reasoning task of whether a given formula holds in a given evolving knowledge base. In particular, we present finitary characterizations of the evolution for certain classes of framework instances, which can be exploited for obtaining decidability results. In more detail, we characterize the complexity of reasoning for some meaningful classes of evolving knowledge bases, ranging from polynomial to double exponential space complexity.

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Section: Introductionmentioning

confidence: 90%

Section: Capturing Framework For Knowledge Evolutionmentioning

confidence: 99%

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Reasoning about evolving nonmonotonic knowledge bases

Eiter

Fink

Sabbatini

et al. 2005

ACM Trans. Comput. Logic

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“…[24]); -AC L * = {assert(r), retract(r), assert event(r), retract event(r), always assert(r), always retract(r), always assert event(r), always retract event(r), cancel assert(r), cancel retract(r) | r ∈ L A }, where the commands have the meaning as described in [24];…”

Section: Dynamic Logic Programs Lups and Lups *mentioning

confidence: 99%

“…and cond ∈ Bel ⊕ (s)}; -ρ L * is given as in [24]; like before, ρ L * (s, A) adds a program P n+1 to the sequence of programs (P 0 , . .…”

Section: Dynamic Logic Programs Lups and Lups *mentioning

confidence: 99%

Reasoning about Evolving Nonmonotonic Knowledge Bases

Eiter

Fink

Sabbatini

et al. 2001

Logic for Programming, Artificial Intelligence, and Reasoning

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Abstract. Recently, several approaches to updating knowledge bases modeled as extended logic programs have been introduced, ranging from basic methods to incorporate (sequences of) sets of rules into a logic program, to more elaborate methods which use an update policy for specifying how updates must be incorporated. In this paper, we introduce a framework for reasoning about evolving knowledge bases, which are represented as extended logic programs and maintained by an update policy. We first describe a formal model which captures various update approaches, and we define a logical language for expressing properties of evolving knowledge bases. We then investigate semantical and computational properties of our framework, where we focus on properties of knowledge states with respect to the canonical reasoning task of whether a given formula holds on a given evolving knowledge base. In particular, we present finitary characterizations of the evolution for certain classes of framework instances, which can be exploited for obtaining decidability results. In more detail, we characterize the complexity of reasoning for some meaningful classes of evolving knowledge bases, ranging from polynomial to double exponential space complexity.

show abstract