Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Negation as failure using tight derivations for general logic programs
Journal of Logic Programming
An analysis of loop checking mechanisms for logic programs
Theoretical Computer Science
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Probabilistic logic programming
Information and Computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Real-world applications of Bayesian networks
Communications of the ACM
Tabled evaluation with delaying 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
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Loop checks for logic programs with functions
Theoretical Computer Science
A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
First-Order Bayesian Reasoning
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
Constructing Flexible Dynamic Belief Networks from First-Order Probalistic Knowledge Bases
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Stochastic Logic Programs: Sampling, Inference and Applications
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Semantics and Inference for Recursive Probability Models
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The complexity of relational query languages (Extended Abstract)
STOC '82 Proceedings of the fourteenth annual ACM symposium on Theory of computing
Bayesian Logic Programs
Learning statistical models from relational data
Learning statistical models from relational data
A dynamic approach to characterizing termination of general logic programs
ACM Transactions on Computational Logic (TOCL)
ACM SIGKDD Explorations Newsletter
PRL: A probabilistic relational language
Machine Learning
Machine Learning
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth 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
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Belief Logic Programming with Cyclic Dependencies
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Model failure and context switching using logic-based stochastic
Journal of Computer Science and Technology
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Recursive loops in a logic program present a challenging problem to the PLP (Probabilistic Logic Programming) framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they may generate cyclic influences, which are disallowed in Bayesian networks. Therefore, in existing PLP approaches, logic programs with recursive loops are considered to be problematic and thus are excluded. In this article, we propose a novel solution to this problem by making use of recursive loops to build a stationary dynamic Bayesian network. We introduce a new PLP formalism, called a Bayesian knowledge base. It allows recursive loops and contains logic clauses of the form A ← A1,…,Al, true, Context, Types, which naturally formulate the knowledge that the Ais have direct influences on A in the context Context under the type constraints Types. We use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. This establishes a clear declarative semantics for a Bayesian knowledge base. We view a logic program with recursive loops as a special temporal model, where backward-chaining cycles of the form A← … A← … are interpreted as feedbacks. This extends existing PLP approaches, which mainly aim at (nontemporal) relational models.