Anytime deduction for probabilistic logic

Frisch, Alan M.; Haddawy, Peter

doi:10.1016/0004-3702(94)90079-5

Cited by 88 publications

(68 citation statements)

References 12 publications

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“…Its roots can be traced back to Boole in 1854 [11]. There is a wide spectrum of formal languages that have been explored in probabilistic logic, ranging from constraints for unconditional and conditional events to rich languages that specify linear inequalities over events (see especially the work by Nilsson [100], Fagin et al [32], Dubois and Prade et al [23,28,2,27], Frisch and Haddawy [34], and Lukasiewicz [83,84,86]; see also the survey on sentential probability logic by Hailperin [41]). Recently, nonmonotonic generalizations of probabilistic logic have been developed and explored; see especially [88] for an overview.…”

Section: Probabilistic Logicmentioning

confidence: 99%

“…One of the earliest approaches to probabilistic description logics is due to Heinsohn [47], who presents a probabilistic extension of the description logic ALC, which allows to represent terminological probabilistic knowledge about concepts and roles, and which is based on the notion of logical entailment in probabilistic logics, similar to [100,2,34,84]. Heinsohn [47], however, does not allow for assertional (classical or probabilistic) knowledge about concept and role instances.…”

Section: Probabilistic Description Logicsmentioning

confidence: 99%

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Managing uncertainty and vagueness in description logics for the Semantic Web

Lukasiewicz

Straccia

2008

Journal of Web Semantics

328

101

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a b s t r a c tOntologies play a crucial role in the development of the Semantic Web as a means for defining shared terms in web resources. They are formulated in web ontology languages, which are based on expressive description logics. Significant research efforts in the semantic web community are recently directed towards representing and reasoning with uncertainty and vagueness in ontologies for the Semantic Web. In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web.

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Section: Probabilistic Logicmentioning

confidence: 99%

Section: Probabilistic Description Logicsmentioning

confidence: 99%

Managing uncertainty and vagueness in description logics for the Semantic Web

Lukasiewicz

Straccia

2008

Journal of Web Semantics

328

101

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“…Artificial intelligence has witnessed steady interest in sets of probability measures, for example, in the theory of probabilistic logic [17,18,19], in DempsterShafer theory [20], in theories of argumentation [21], and in techniques that generalize graph-theoretic models such as Bayesian networks [22,23,24]. 1 Our contribution here is to identify the planning under uncertainty spectrum with the theory of sets of probability measures.…”

Section: Risk Knightian Uncertainty and Sets Of Probabilitiesmentioning

confidence: 99%

Unifying Nondeterministic and Probabilistic Planning Through Imprecise Markov Decision Processes

Trevizan

Cozman

Barros

2006

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

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Abstract. This paper proposes an unifying formulation for nondeterministic and probabilistic planning. These two strands of AI planning have followed different strategies: while nondeterministic planning usually looks for minimax (or worst-case) policies, probabilistic planning attempts to maximize expected reward. In this paper we show that both problems are special cases of a more general approach, and we demonstrate that the resulting structures are Markov Decision Processes with Imprecise Probabilities (MDPIPs). We also show how existing algorithms for MDPIPs can be adapted to planning under uncertainty.

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“…There is a wide spectrum of formal languages that have been explored in model-theoretic probabilistic logic, ranging from constraints for unconditional and conditional events [15,19,2,18,23,33,47,41,42,44,48] to rich languages that specify linear inequalities over events [21]. The main algorithmic tasks related to model-theoretic probabilistic logic are deciding satisfiability, deciding logical consequence, and computing tight logically entailed intervals.…”

Section: Introductionmentioning

confidence: 99%