Logical Probability Preferences
This work addresses a foundational gap in AI for logical reasoning with probability preferences, though it appears incremental as it builds upon existing answer set programming methods.
The paper tackles the problem of representing and reasoning about both quantitative and qualitative probability preferences by introducing probability answer set optimization programs, a unified logical framework, and demonstrates its application to a nurse restoring problem variant with probability preferences.
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.