TL;DR
This paper introduces a unified logical framework called probability answer set optimization programs that models both quantitative and qualitative probability preferences, demonstrated through a nurse restoring problem variant.
Contribution
It is the first to provide a logical framework for reasoning about both probability quantitative and qualitative preferences together.
Findings
Successfully applied to a nurse restoring with probability preferences problem
First logical framework for combined probability quantitative and qualitative preferences
Demonstrates the framework's effectiveness in complex decision-making scenarios
Abstract
We present a unified logical framework for representing and reasoning about both probability quantitative and qualitative preferences in probability answer set programming, called probability answer set optimization programs. The proposed framework is vital to allow defining probability quantitative preferences over the possible outcomes of qualitative preferences. We show the application of probability answer set optimization programs to a variant of the well-known nurse restoring problem, called the nurse restoring with probability preferences problem. To the best of our knowledge, this development is the first to consider a logical framework for reasoning about probability quantitative preferences, in general, and reasoning about both probability quantitative and qualitative preferences in particular.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Constraint Satisfaction and Optimization