The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Knowledge, probability, and adversaries
Journal of the ACM (JACM)
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Causality in commonsense reasoning about actions
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AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
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ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
SLGAD Resolution for Inference on Logic Programs with Annotated Disjunctions
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ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Extending probLog with continuous distributions
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning the parameters of probabilistic logic programs from interpretations
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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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.