The Independent Choice Logic and Beyond

Poole, David

doi:10.1007/978-3-540-78652-8_8

Cited by 72 publications

(49 citation statements)

References 31 publications

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“…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%

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: 99%

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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“…Besides Bayesian approaches, other graphical models have also been proposed, such as MLN [83]. Logic program-based approaches include ICL [80] and SLP [67,68]. We will discuss and compare this closely related work in more detail.…”

Section: First-order Probabilistic Modelsmentioning

confidence: 99%

“…The Independent Choice Logic (ICL) [80] can be seen as adding independent stochastic inputs to a logic program, with a look similar to SLP. From another perspective, it can be regarded as a way of rule-based specification of Bayesian networks with logical variables.…”

Section: Non-bayesian Network Based Approachesmentioning

confidence: 99%

On the combination of logical and probabilistic models for information analysis

et al. 2011

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Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domain-specific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemes not supportable directly in Horn clause logic. We compare our approach to other ones, including some that use non-standard logics.

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“…Many probabilistic logic programming languages, including PRISM (Sato and Kameya 2001), Logic Programs with Annotated Disjunctions (LPADs) (Vennekens et al 2004), ProbLog (De Raedt et al 2007, and ICL (Poole 2008), are probabilistic extensions of Prolog based on Sato's distribution semantics (Sato 1995). Specifically, such languages introduce probabilistic truth values into Prolog, such as probabilistic facts, which are true (or false) with a certain probability, or probabilistic choices, that is, groups of facts only one of which is true at any point.…”

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confidence: 99%

Introduction to the special issue on probability, logic and learning

Cussens

Raedt

Kimmig

et al. 2014

Theory and Practice of Logic Programming

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Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well.

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The Independent Choice Logic and Beyond

Cited by 72 publications

References 31 publications

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

On the combination of logical and probabilistic models for information analysis

Introduction to the special issue on probability, logic and learning

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