First order compiler: A deterministic logic program synthesis algorithm
Journal of Symbolic Computation
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
From Logic to Logic Programming
From Logic to Logic Programming
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Fixpoint semantics for logic programming a survey
Theoretical Computer Science
Stochastic attribute-value grammars
Computational Linguistics
Semi-naive evaluation in linear tabling
PPDP '04 Proceedings of the 6th ACM SIGPLAN international conference on Principles and practice of declarative programming
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Structured machine learning: the next ten years
Machine Learning
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
Logic-Based Probabilistic Modeling
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
New advances in logic-based probabilistic modeling by PRISM
Probabilistic inductive logic programming
CLP(BN): constraint logic programming for probabilistic knowledge
Probabilistic inductive logic programming
Inference with constrained hidden markov models in prism
Theory and Practice of Logic Programming
BET: an inductive logic programming workbench
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Data Mining and Knowledge Discovery
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PRISM is a logic-based Turing-complete symbolic-statistical modeling language with a built-in parameter learning routine. In this paper,we enhance the modeling power of PRISM by allowing general PRISM programs to fail in the generation process of observable events. Introducing failure extends the class of definable distributions but needs a generalization of the semantics of PRISM programs. We propose a three valued probabilistic semantics and show how failure enables us to pursue constraint-based modeling of complex statistical phenomena.