CP-logic
CP-logic: A language of causal probabilistic events and its relation to logic programming This paper develops a logical language for representing probabilistic causal laws. Our interest in such a language is two-fold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shaferâ€™s by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.
This software is also peer reviewed by journal TOMS.
This software is also peer reviewed by journal TOMS.
Keywords for this software
References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
Sorted by year (- Buchman, David; Poole, David: Negative probabilities in probabilistic logic programs (2017)
- Riguzzi, Fabrizio; Cota, Giuseppe; Bellodi, Elena; Zese, Riccardo: Causal inference in cplint (2017)
- Riguzzi, Fabrizio: The distribution semantics for normal programs with function symbols (2016)
- Vlasselaer, Jonas; Van den Broeck, Guy; Kimmig, Angelika; Meert, Wannes; De Raedt, Luc: $T_\mathcalP$-compilation for inference in probabilistic logic programs (2016)
- De Raedt, Luc; Kimmig, Angelika: Probabilistic (logic) programming concepts (2015)
- Michels, Steffen; Hommersom, Arjen; Lucas, Peter J.F.; Velikova, Marina: A new probabilistic constraint logic programming language based on a generalised distribution semantics (2015)
- Van Ranst, Wiebe; Vennekens, Joost: An OpenCL implementation of a forward sampling algorithm for CP-logic (2015)
- Gavanelli, Marco; Riguzzi, Fabrizio; Milano, Michela; Cagnoli, Paolo: Logic-based decision support for strategic environmental assessment (2010)
- Riguzzi, Fabrizio; Swift, Terrance: Tabling and answer subsumption for reasoning on logic programs with annotated disjunctions (2010)
- Vennekens, Joost; Bruynooghe, Maurice; Denecker, Marc: Embracing events in causal modelling: interventions and counterfactuals in CP-logic (2010)
- Vennekens, Joost; Denecker, Marc; Bruynooghe, Maurice: CP-logic: A language of causal probabilistic events and its relation to logic programming (2009)
- Vennekens, Joost; Denecker, Marc; Bruynooghe, Maurice: CP-logic: A language of causal probabilistic events and its relation to logic programming (2009) ioport