Probabilistic Inductive Logic Programming Based on Answer Set Programming
This work addresses the need for expressive probabilistic representation in logic programming, but it appears incremental as it builds on existing ASP methods without demonstrating broad SOTA impact.
The authors tackled the problem of representing probabilistic knowledge by proposing a new formal language based on Answer Set Programming (ASP), which allows for probabilistic annotations and learning from data, with a prototypical implementation and examples provided.
We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.