Logical Fuzzy Preferences
This work addresses the problem of integrating quantitative and qualitative preferences in fuzzy logic systems for researchers in computational logic and AI planning.
The authors developed a unified logical framework called fuzzy answer set optimization programs to represent and reason about both quantitative and qualitative preferences in fuzzy answer set programming, applying it to course scheduling with fuzzy preferences.
We present a unified logical framework for representing and reasoning about both quantitative and qualitative preferences in fuzzy answer set programming, called fuzzy answer set optimization programs. The proposed framework is vital to allow defining quantitative preferences over the possible outcomes of qualitative preferences. We show the application of fuzzy answer set optimization programs to the course scheduling with fuzzy preferences problem. To the best of our knowledge, this development is the first to consider a logical framework for reasoning about quantitative preferences, in general, and reasoning about both quantitative and qualitative preferences in particular.