Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An analysis of first-order logics of probability
Artificial Intelligence
Planning and control
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
ACM Computing Surveys (CSUR)
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Learning probabilistic relational models
Relational Data Mining
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Efficient EM Learning with Tabulation for Parameterized Logic Programs
CL '00 Proceedings of the First International Conference on Computational Logic
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Adaptive Bayesian Logic Programs
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Towards Combining Inductive Logic Programming with Bayesian Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
A Graphical Method for Parameter Learning of Symbolic-Statistical Models
DS '99 Proceedings of the Second International Conference on Discovery Science
Building large knowledge bases by mass collaboration
Proceedings of the 2nd international conference on Knowledge capture
ACM SIGKDD Explorations Newsletter
Learning first-order probabilistic models with combining rules
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discovery of empirical theories based on the measurement theory
Minds and Machines - Machine learning as experimental philosophy of science
Parameter learning for relational Bayesian networks
Proceedings of the 24th international conference on Machine learning
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
The Complexity of Translating BLPs to RMMs
Inductive Logic Programming
A Survey of Formal Verification for Business Process Modeling
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
A Comparison between Two Statistical Relational Models
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A methodology for in-network evaluation of integrated logical-statistical models
Proceedings of the 6th ACM conference on Embedded network sensor systems
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Learning first-order probabilistic models with combining rules
Annals of Mathematics and Artificial Intelligence
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Revision of first-order Bayesian classifiers
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Combining clauses with various precisions and recalls to produce accurate probabilistic estimates
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Probabilistic inductive logic programming
Probabilistic inductive 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
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
Exploiting causal independence in Markov logic networks: combining undirected and directed models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
PFORTE: revising probabilistic FOL theories
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Negation elimination for finite PCFGs
LOPSTR'04 Proceedings of the 14th international conference on Logic Based Program Synthesis and Transformation
Probabilistic first-order theory revision from examples
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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First-order logic is the traditional basis for knowledge representation languages. However, its applicability to many real-world tasks is limited by its inability to represent uncertainty. Bayesian belief networks, on the other hand, are inadequate for complex KR tasks due to the limited expressivity of the underlying (prepositional) language. The need to incorporate uncertainty into an expressive language has led to a resurgence of work on first-order probabilistic Logic. This paper addresses one of the main objections to the incorporation of probabilities into the language: "Where do the numbers come from?" We present an approach that takes a knowledge base in an expressive rule-based first-order language, and leams the probabilistic parameters associated with those rules from data cases. Our approach, which is based on algorithms for learning in traditional Bayesian networks, can handle data cases where many of the relevant aspects of the situation are unobserved. It is also capable of utilizing a rich variety of data cases, including instances with varying causal structure, and even involving a varying number of individuals. These features allow the approach to be used for a wide range of tasks, such as learning genetic propagation models or learning first-order STRIPS planning operators with uncertain effects.