TL;DR
This paper introduces a logical framework using fuzzy answer set optimization programming with aggregates to model and solve fuzzy optimization problems, demonstrated through a fuzzy water allocation case study.
Contribution
It presents a novel logical approach incorporating fuzzy aggregates into answer set programming for fuzzy optimization problems.
Findings
Successfully models fuzzy water allocation problem
Enables minimization and maximization in fuzzy environments
Provides a flexible logical framework for fuzzy optimization
Abstract
We present a logical framework to represent and reason about fuzzy optimization problems based on fuzzy answer set optimization programming. This is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and maximum in the language of fuzzy answer set optimization programming to allow minimization or maximization of some desired criteria under fuzzy environments. We show the application of the proposed logical fuzzy optimization framework under the fuzzy answer set optimization programming to the fuzzy water allocation optimization problem.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · AI-based Problem Solving and Planning · Advanced Algebra and Logic