CP-logic: A language of causal probabilistic events and its relation to logic programming

Vennekens, Joost; Denecker, Marc; Bruynooghe, Maurice

doi:10.1017/s1471068409003767

Cited by 79 publications

(106 citation statements)

References 39 publications

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“…Unfortunately, PGMs are unsuitable for capturing knowledge that goes beyond statistical dependence and independence information, like clinical guidelines. In contrast, it has been shown that CP-logic that the probabilistic verification introduced in this chapter is based upon, can also represent various PGMs [19,39].…”

Section: Probabilistic Verificationmentioning

confidence: 96%

“…In [19], the semantics of this CP-logic is presented by relating the set of laws to a possible probabilistic causal process. We briefly introduce the semantics of CPDL in the spirit of CP-logic.…”

Section: Causal Probabilistic Decision Logicmentioning

confidence: 99%

“…For example, there now exist logical versions of Markov networks, called Markov logic networks [40], and of Bayesian networks, called Bayesian logic programs [41]. Influential is also Poole's independent choice logic [42,43], in addition to ProbLog [44] and CP-Logic [19]. These probabilistic logics offer a very natural and flexible choice for modelling complex domains involving uncertainty.…”

Section: Probabilistic Verificationmentioning

confidence: 99%

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A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Anselma

Bottrighi

Giordano

et al. 2015

Foundations of Biomedical Knowledge Representation

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Abstract. Computer Interpretable Guidelines (CIGs) are assuming a major role in the medical area, in order to enhance the quality of medical assistance by providing physicians with evidence-based recommendations. However, the complexity of CIGs (which may contain hundreds of related clinical activities) demands for a verification process, aimed at assuring that a CIG satisfies several different types of properties (e.g., verification of the CIG correctness with respect to several criteria). Verification is a demanding task, which may be enhanced through the adoption of advanced Artificial Intelligence techniques. In this paper, we propose a general and hybrid approach to address such a task, suggesting that, given the heterogeneous character of the knowledge in CIGs, different forms of verification should be supported, through the adoption of proper (and different) methodologies.

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Section: Probabilistic Verificationmentioning

confidence: 96%

Section: Causal Probabilistic Decision Logicmentioning

confidence: 99%

Section: Probabilistic Verificationmentioning

confidence: 99%

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A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Anselma

Bottrighi

Giordano

et al. 2015

Foundations of Biomedical Knowledge Representation

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“…It underlies many probabilistic logic programming languages such as Probabilistic Horn Abduction (Poole, 1993), PRISM (Sato, 1995;Sato and Kameya, 2001), Independent Choice Logic (Poole, 1997), Logic Programs with Annotated Disjunctions (Vennekens et al, 2004), ProbLog (De Raedt et al, 2007) and CP-logic (Vennekens et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Description Logics under the distribution semantics

Riguzzi

Bellodi

Lamma

et al. 2015

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Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.

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“…In this semantic framework, logic programming (LP) is used to define a probability distribution over a set of binary facts. Examples of languages based on that semantics are ProbLog [6], PRISM [7], ICL [8], and CP-logic [9]. The choice for this kind of semantics is motivated by the fact that it allows one to use probabilities with local meaning, which provides the modularity needed for knowledge representation, similar to the widely used Bayesian networks [10].…”

Section: Introductionmentioning

confidence: 99%

A new probabilistic constraint logic programming language based on a generalised distribution semantics

Michels

Hommersom

Lucas

et al. 2015

Artificial Intelligence

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Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. We focus on a class of probabilistic logic based on Sato's distribution semantics, which extends logic programming with probability distributions on binary random variables and guarantees a unique probability distribution. For many applications binary random variables are, however, not sufficient and one requires random variables with arbitrary ranges, e.g. real numbers. We tackle this problem by developing a generalised distribution semantics for a new probabilistic constraint logic programming language. In order to perform exact inference, imprecise probabilities are taken as a starting point, i.e. we deal with sets of probability distributions rather than a single one. It is shown that given any continuous distribution, conditional probabilities of events can be approximated arbitrarily close to the true probability. Furthermore, for this setting an inference algorithm that is a generalisation of weighted model counting is developed, making use of SMT solvers. We show that inference has similar complexity properties as precise probabilistic inference, unlike most imprecise methods for which inference is more complex. We also experimentally confirm that our algorithm is able to exploit local structure, such as determinism, which further reduces the computational complexity.

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CP-logic: A language of causal probabilistic events and its relation to logic programming

Cited by 79 publications

References 39 publications

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Probabilistic Description Logics under the distribution semantics

A new probabilistic constraint logic programming language based on a generalised distribution semantics

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