Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy (Full Version)
This work addresses the challenge of handling uncertain knowledge in knowledge representation, which is a foundational problem in AI, though it appears incremental as it builds on existing description logics and probabilistic reasoning methods.
The paper tackles the problem of representing and reasoning with uncertain, context-dependent knowledge by introducing the probabilistic description logic ALCP, which combines logical and probabilistic dependencies, and employs the principle of maximum entropy for probabilistic conclusions, showing that it satisfies desirable properties of probabilistic logics.
A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic ALCP that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. ALCP allows the expression of logical dependencies on the domain and probabilistic dependencies on the possible contexts. In order to draw probabilistic conclusions, we employ the principle of maximum entropy. We provide reasoning algorithms for this logic, and show that it satisfies several desirable properties of probabilistic logics.