Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
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
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
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
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
Bayesian Logic Programs
Learning statistical models from relational data
Learning statistical models from relational data
ACM SIGKDD Explorations Newsletter
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Stochastic logic programs: sampling, inference and applications
UAI'00 Proceedings of the Sixteenth 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
Using first-order probability logic for the construction of Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
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Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they 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 paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form A ← A1,...,Al, true, Context, Types, which naturally represents the knowledge that the Ais have direct influences on A in the context Context under the type constraints Types. We then 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. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian network from a logic program are developed.