Causal Logic Programming

Cabalar, Pedro

doi:10.1007/978-3-642-30743-0_8

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Cause-effect relations are an important part of human knowledge. There exist a number of knowledge representation languages (McCain and Turner 1996;Vennekens et al 2009;Cabalar 2012) in which logic programming style rules are used to represent such relations. The basic idea in all these approaches is that the head of such a rule represents an effect that is caused by its body.…”

Section: Introductionmentioning

confidence: 99%

FO(C): A Knowledge Representation Language of Causality

Bogaerts,

Vennekens,

Denecker

et al. 2014

Preprint

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Cause-effect relations are an important part of human knowledge. In real life, humans often reason about complex causes linked to complex effects. By comparison, existing formalisms for representing knowledge about causal relations are quite limited in the kind of specifications of causes and effects they allow. In this paper, we present the new language C-Log, which offers a significantly more expressive representation of effects, including such features as the creation of new objects. We show how C-Log integrates with first-order logic, resulting in the language FO(C). We also compare FO(C) with several related languages and paradigms, including inductive definitions, disjunctive logic programming, business rules and extensions of Datalog.

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Section: Introductionmentioning

confidence: 99%

FO(C): A Knowledge Representation Language of Causality

Bogaerts,

Vennekens,

Denecker

et al. 2014

Preprint

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“…A frequent informal way of explaining the effect of default negation in an introductory class on semantics in logic programming (LP) is that a literal of the form 'not p' should be read as "there is no way to derive p." Although this idea seems quite intuitive, it is actually using a concept outside the discourse of any of the existing LP semantics: the ways to derive p. To explore this idea, [1] introduced the so-called causal logic programs. The semantics was an extension of stable models [2] relying on the idea of "justification" or "proof".…”

Section: Introductionmentioning

confidence: 99%

An Algebra of Causal Chains

Cabalar,

Fandinno

2013

Preprint

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In this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications, in a similar spirit than a set of proof trees. The main contribution of this paper is that we capture justifications into an algebra of truth values with three internal operations: an addition '+' representing alternative justifications for a formula, a commutative product ' * ' representing joint interaction of causes and a non-commutative product '•' acting as a concatenation or proof constructor. Using this multi-valued semantics, we obtain a one-to-one correspondence between the syntactic proof tree of a standard (non-causal) logic program and the interpretation of each true atom in a model. Furthermore, thanks to this algebraic characterization we can detect semantic properties like redundancy and relevance of the obtained justifications. We also identify a lattice-based characterization of this algebra, defining a direct consequences operator, proving its continuity and that its least fix point can be computed after a finite number of iterations. Finally, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct.

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