Cost-based query answering in action probabilistic logic programs

Authors:
Gerardo I. Simari;John P. Dickerson;V. S. Subrahmanian
Affiliations:
Department of Computer Science and UMIACS, University of Maryland College Park, College Park, MD;Department of Computer Science and UMIACS, University of Maryland College Park, College Park, MD;Department of Computer Science and UMIACS, University of Maryland College Park, College Park, MD
Venue:
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Year:
2010

Citing 14
Cited 0

Probabilistic logic

Artificial Intelligence
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
Probabilistic Horn abduction and Bayesian networks

Artificial Intelligence
Feature-based methods for large scale dynamic programming

Machine Learning - Special issue on reinforcement learning
The independent choice logic for modelling multiple agents under uncertainty

Artificial Intelligence - Special issue on economic principles of multi-agent systems
Stochastic dynamic programming with factored representations

Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming

Markov Decision Processes: Discrete Stochastic Dynamic Programming
A Survey of Optimization by Building and Using Probabilistic Models

Computational Optimization and Applications
Algorithms for sequential decision-making

Algorithms for sequential decision-making
Combining probabilistic logic programming with the power of maximum entropy

Artificial Intelligence - Special issue on nonmonotonic reasoning
Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds

Annals of Mathematics and Artificial Intelligence
Implementing Probabilistic Abductive Logic Programming with Constraint Handling Rules

Constraint Handling Rules

Quantified Score

Hi-index	0.00

Visualization

Abstract

Action-probabilistic logic programs (ap-programs), a class of probabilistic logic programs, have been applied during the last few years for modeling behaviors of entities. Rules in ap-programs have the form "If the environment in which entity E operates satisfies certain conditions, then the probability that E will take some action A is between L and U". Given an ap-program, we have addressed the problem of deciding if there is a way to change the environment (subject to some constraints) so that the probability that entity E takes some action (or combination of actions) is maximized. In this work we tackle a related problem, in which we are interested in reasoning about the expected reactions of the entity being modeled when the environment is changed. Therefore, rather than merely deciding if there is a way to obtain the desired outcome, we wish to find the best way to do so, given costs of possible outcomes. This is called the Cost-based Query Answering Problem (CBQA). We first formally define and study an exact (intractable) approach to CBQA, and then go on to propose a more efficient algorithm for a specific subclass of ap-programs that builds on past work in a basic version of this problem.