Artificial Intelligence
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
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Probabilistic logic programming
Information and Computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Semantics and complexity of abduction from default theories
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Abduction from logic program: semantics and complexity
Theoretical Computer Science
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
Probabilistic Argumentation Systems and Abduction
Annals of Mathematics and Artificial Intelligence
Structural and Probabilistic Knowledge for Abductive Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abduction in Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
An Abductive Proof Procedure for Reasoning About Actions in Modal Logic Programming
NMELP '96 Selected papers from the Non-Monotonic Extensions of Logic Programming
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
Annals of Mathematics and Artificial Intelligence
Implementing Probabilistic Abductive Logic Programming with Constraint Handling Rules
Constraint Handling Rules
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Solving H-horizon, stationary Markov decision problems in time proportional to log(H)
Operations Research Letters
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Action-probabilistic logic programs (ap-programs) are a class of probabilistic logic programs that have been extensively used 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 are interested in trying to change the environment, subject to some constraints, so that the probability that entity E takes some action (or combination of actions) is maximized. This is called the Basic Abductive Query Answering Problem (BAQA). We first formally define and study the complexity of BAQA, and then go on to provide an exact (exponential time) algorithm to solve it, followed by more efficient algorithms for specific subclasses of the problem. We also develop appropriate heuristics to solve BAQA efficiently. The second problem, called the Cost-based Query Answering (CBQA) problem checks to see if there is some way of achieving a desired action (or set of actions) with a probability exceeding a threshold, given certain costs. 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 the results for the basic version of this problem. We also develop the first algorithms for parallel evaluation of CBQA. We conclude with an extensive report on experimental evaluations performed over prototype implementations of the algorithms developed for both BAQA and CBQA, showing that our parallel algorithms work well in practice.