Journal of the ACM (JACM)
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Probabilistic logic programming
Information and Computation
The alternating fixpoint of logic programs with negation
PODS '89 Selected papers of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Stable semantics for probabilistic deductive databases
Information and Computation
Computing annotated logic programs
Proceedings of the eleventh international conference on Logic programming
Mixed integer programming methods for computing nonmonotonic deductive databases
Journal of the ACM (JACM)
Foundations of logic programming
Principles of knowledge representation
Artificial Intelligence
WFS + Branch and Bound = Stable Models
IEEE Transactions on Knowledge and Data Engineering
Computation of Stable Models and Its Integration with Logical Query Processing
IEEE Transactions on Knowledge and Data Engineering
On a theory of probabilistic deductive databases
Theory and Practice of Logic Programming
Hybrid probabilistic programs with non-monotonic negation: semantics and algorithms
Hybrid probabilistic programs with non-monotonic negation: semantics and algorithms
Hybrid probabilistic programs: algorithms and complexity
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Hybrid probabilistic logic programs with non-monotonic negation
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Towards a more practical hybrid probabilistic logic programming framework
PADL'05 Proceedings of the 7th international conference on Practical Aspects of Declarative Languages
Probabilistic Planning in Hybrid Probabilistic Logic Programs
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
A Logical Framework to Reinforcement Learning Using Hybrid Probabilistic Logic Programs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Probabilistic Reasoning by SAT Solvers
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In [22], a stable model semantics extension of the language of hybrid probabilistic logic programs [21] with non-monotonic negation, normal hybrid probabilistic programs (NHPP), has been developed by introducing the notion of stable probabilistic model semantics. It has been shown in [22] that the stable probabilistic model semantics is a natural extension of the stable model semantics for normal logic programs and the language of normal logic programs is a subset of the language NHPP. This suggests that efficient algorithms and implementations for computing the stable probabilistic model semantics for NHPP can be developed by extending the efficient algorithms and implementation for computing the stable model semantics for normal logic programs, e.g., SMODELS [17]. In this paper, we explore an algorithm for computing the stable probabilistic model semantics for NHPP along with its auxiliary functions. The algorithm we develop is based on the SMODELS [17] algorithms. We show the soundness and completeness of the proposed algorithm. We provide the necessary conditions that these auxiliary functions have to satisfy to guarantee the soundness and completeness of the proposed algorithm. This algorithm is the first to develop for studying computational methods for computing the stable probabilistic models semantics for hybrid probabilistic logic programs with non-monotonic negation.