Artificial Intelligence
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Probabilistic logic programming
Information and Computation
Heterogeneous active agents, I: semantics
Artificial Intelligence
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
Column Generation Methods for Probabilistic Logic
Proceedings of the 1st Integer Programming and Combinatorial Optimization Conference
Probabilistic Logic Programming and Bayesian Networks
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
Computing Non-Ground Representations of Stable Models
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Optimal Models of Disjunctive Logic Programs: Semantics, Complexity, and Computation
IEEE Transactions on Knowledge and Data Engineering
Optimal status sets of heterogeneous agent programs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
CARA: A Cultural-Reasoning Architecture
IEEE Intelligent Systems
Scaling Most Probable World Computations in Probabilistic Logic Programs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Stochastic Reasoning with Models of Agent Behavior
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Using Histograms to Better Answer Queries to Probabilistic Logic Programs
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
An AGM-style belief revision mechanism for probabilistic spatio-temporal logics
Artificial Intelligence
Annotated probabilistic temporal logic
ACM Transactions on Computational Logic (TOCL)
Cost-based query answering in action probabilistic logic programs
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Focused most probable world computations in probabilistic logic programs
Annals of Mathematics and Artificial Intelligence
Mining actionable behavioral rules
Decision Support Systems
Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
ACM Transactions on Computational Logic (TOCL)
Parallel Abductive Query Answering in Probabilistic Logic Programs
ACM Transactions on Computational Logic (TOCL)
| Hi-index | 0.00 |
The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics. We propose a variant of PLPs called action probabilistic logic programs or -programs that use a two-sorted alphabet to describe the conditions under which certain real-world entities take certain actions. In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find the most probable world (MPW) for -programs. In contrast, past work on PLPs has primarily focused on the problem of entailment. This paper quickly presents the syntax and semantics of -programs and then shows a naive algorithm to solve the MPW problem using the linear program formulation commonly used for PLPs. As such linear programs have an exponential number of variables, we present two important new algorithms, called $ \textsf{HOP} $ and $ \textsf{SemiHOP} $ to solve the MPW problem exactly. Both these algorithms can significantly reduce the number of variables in the linear programs. Subsequently, we present a "binary" algorithm that applies a binary search style heuristic in conjunction with the Naive, $ \textsf{HOP} $ and $ \textsf{SemiHOP} $ algorithms to quickly find worlds that may not be "most probable." We experimentally evaluate these algorithms both for accuracy (how much worse is the solution found by these heuristics in comparison to the exact solution) and for scalability (how long does it take to compute). We show that the results of $ \textsf{SemiHOP} $ are very accurate and also very fast: more than 1030,000 worlds can be handled in a few minutes. Subsequently, we develop parallel versions of these algorithms and show that they provide further speedups.