Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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)
Fundamentals of speech recognition
Fundamentals of speech recognition
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
XSB as an efficient deductive database engine
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Complex Probabilistic Modeling with Recursive Relational Bayesian Networks
Annals of Mathematics and Artificial Intelligence
OLD Resolution with Tabulation
Proceedings of the Third International Conference on Logic Programming
Proceedings of the 17th International Conference on Logic Programming
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning probabilistic models of link structure
The Journal of Machine Learning Research
PPDP '04 Proceedings of the 6th ACM SIGPLAN international conference on Principles and practice of declarative programming
PRL: A probabilistic relational language
Machine Learning
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Linear tabling strategies and optimizations
Theory and Practice of Logic Programming
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Variational Bayes via propositionalized probability computation in PRISM
Annals of Mathematics and Artificial Intelligence
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Inside-outside probability computation for belief propagation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Generative modeling with failure in PRISM
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic inductive logic programming
Probabilistic inductive logic programming
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
The language features and architecture of b-prolog
Theory and Practice of Logic Programming - Prolog Systems
What are the Odds?: probabilistic programming in Scala
Proceedings of the 4th Workshop on Scala
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PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic modeling capable of learning statistical parameters from observed data. After reviewing it from various viewpoints, we examine some technical details related to logic programming, including semantics, search and program synthesis.