Integrating Belief Domains into Probabilistic Logic Programs
This addresses the problem of handling epistemic uncertainty in PLP for AI researchers and practitioners, representing an incremental advancement.
The paper tackles the limitation of Probabilistic Logic Programming (PLP) in expressing epistemic uncertainty by introducing interval-based Capacity Logic Programs that extend the Distribution Semantics to include belief functions, resulting in a framework suitable for practical applications.
Probabilistic Logic Programming (PLP) under the Distribution Semantics is a leading approach to practical reasoning under uncertainty. An advantage of the Distribution Semantics is its suitability for implementation as a Prolog or Python library, available through two well-maintained implementations, namely ProbLog and cplint/PITA. However, current formulations of the Distribution Semantics use point-probabilities, making it difficult to express epistemic uncertainty, such as arises from, for example, hierarchical classifications from computer vision models. Belief functions generalize probability measures as non-additive capacities, and address epistemic uncertainty via interval probabilities. This paper introduces interval-based Capacity Logic Programs based on an extension of the Distribution Semantics to include belief functions, and describes properties of the new framework that make it amenable to practical applications.