Logic Programs with Annotated Disjunctions

Vennekens, Joost; Verbaeten, Sofie; Bruynooghe, Maurice

doi:10.1007/978-3-540-27775-0_30

Cited by 143 publications

(184 citation statements)

References 18 publications

(19 reference statements)

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“…In the second part of this paper, we formally investigate this relation. This will lead to a theorem, that shows that our logic almost completely coincides with the probabilistic logic programming language of Logic Programs with Annotated Disjunctions [12], which is known to have strong ties to Poole's Independent Choice Logic [7]. As such, the previous analysis provides additional motivation for these languages, showing that they are not only meaningful combinations of logic programming and probability, but that they also arise naturally out of a desire to represent causal information about probabilistic processes.…”

Section: Introductionmentioning

confidence: 84%

“…Logic Programs with Annotated Disjunctions (LPADs) are a probabilistic logic programming language, that was conceived in [12] as a straightforward extension of logic programs with probability. In this section, we relate LPADs to CP-logic.…”

Section: Logic Programs With Annotated Disjunctionsmentioning

confidence: 99%

“…In [12], LPADs are compared to a number of different probabilistic logic programming formalisms. For instance, it was shown that this logic is very closely related to Poole's Independent Choice Logic.…”

Section: Logic Programs With Annotated Disjunctionsmentioning

confidence: 99%

See 2 more Smart Citations

Representing Causal Information About a Probabilistic Process

Vennekens

Denecker

Bruynooghe

2006

Logics in Artificial Intelligence

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Abstract. We study causal information about probabilistic processes, i.e., information about why events occur. A language is developed in which such information can be formally represented and we investigate when this suffices to uniquely characterize the probability distribution that results from such a process. We examine both detailed representations of temporal aspects and representations in which time is implicit. In this last case, our logic turns into a more fine-grained version of Pearl's approach to causality. We relate our logic to certain probabilistic logic programming languages, which leads to a clearer view on the knowledge representation properties of these language. We show that our logic induces a semantics for disjunctive logic programs, in which these represent non-deterministic processes. We show that logic programs under the well-founded semantics can be seen as a language of deterministic causality, which we relate to McCain & Turner's causal theories.

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Section: Introductionmentioning

confidence: 84%

Section: Logic Programs With Annotated Disjunctionsmentioning

confidence: 99%

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Representing Causal Information About a Probabilistic Process

Vennekens

Denecker

Bruynooghe

2006

Logics in Artificial Intelligence

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“…A similar relationship between SSAT and other probabilistic logic programming frameworks, e.g., (Ng & Subrahmanian 1992;1993;1994;Dekhtyar & Subrahmanian 2000;Kern-Isberner & Lukasiewicz 2004;Lukasiewicz 1998;Baral, Gelfond, & Rushton 2004;Kersting & Raedt 2000;Lakshmanan & Sadri 2001;Poole 1997;Vennekens, Verbaeten, & Bruynooghe 2004), has not been studied. However, the relationship between the probabilistic logic programming frameworks (Ng & Subrahmanian 1992;1993;1994;Dekhtyar & Subrahmanian 2000;Kern-Isberner & Lukasiewicz 2004;Lukasiewicz 1998) and a different extension to SAT, namely, Probabilistic SAT (PSAT) (Boole 1854) has been studied.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

“…However, it is not clear how to translate PSAT to a probabilistic logic program in (Ng & Subrahmanian 1992;1993;1994;Dekhtyar & Subrahmanian 2000;Kern-Isberner & Lukasiewicz 2004;Lukasiewicz 1998). The probabilistic logic programming frameworks of (Baral, Gelfond, & Rushton 2004;Kersting & Raedt 2000;Poole 1997;Vennekens, Verbaeten, & Bruynooghe 2004) relate probabilistic logic programming to Bayesian networks, which is different from SSAT and PSAT.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability

Saad

2008

Lecture Notes in Computer Science

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In this paper we study the relationship between Stochastic Satisfiability (SSAT) (Papadimitriou 1985;Littman, Majercik, & Pitassi 2001) and Extended Hybrid Probabilistic Logic Programs (EHPP) with probabilistic answer set semantics (Saad 2006). We show that any instance of SSAT can be modularly translated into an EHPP program with probabilistic answer set semantics. In addition, we show that there is no modular mapping from EHPP to SSAT. This shows that EHPP is more expressive than SSAT from the knowledge representation point of view. Moreover, we show that the translation in the other way around from a program in EHPP to SSAT is more involved. We show that not every program in EHPP can be translated into an SSAT instance, rather a restricted class of EHPP can be translated into SSAT.

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Probabilistic logic programming on the web

et al. 2015

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We present the web application "cplint on SWISH", that allows the user to write probabilistic logic programs and compute the probability of queries with just a web browser. The application is based on SWISH, a recently proposed web framework for logic programming. SWISH is based on various features and packages of SWI-Prolog, in particular its web server and its Pengine library, that allow to create remote Prolog engines and to pose queries to them. In order to develop the web application, we started from the PITA system which is included in cplint, a suite of programs for reasoning on Logic Programs with Annotated Disjunctions, by porting PITA to SWI-Prolog. Moreover, we modified the PITA library so that it can be executed in a multi-threading environment. Developing "cplint on SWISH" also required modification of the JavaScript SWISH code that creates and\ud queries Pengines. "cplint on SWISH" includes a number of examples that cover a wide range of domains and provide interesting applications of Probabilistic Logic Programming (PLP). By providing a web interface to cplint we allow users to experiment with PLP without the need to install a system, a procedure which is often complex, error prone and limited mainly to the Linux platform. In this way, we aim to reach out to a wider audience and popularize PLP

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Logic Programs with Annotated Disjunctions

Cited by 143 publications

References 18 publications

Representing Causal Information About a Probabilistic Process

Representing Causal Information About a Probabilistic Process

On the Relationship between Hybrid Probabilistic Logic Programs and Stochastic Satisfiability

Probabilistic logic programming on the web

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