Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization

Damiano Azzolini, Fabrizio Riguzzi

Keywords: probabilistic answer set programming, statistical relational artificial intelligence, imprecise probabilities, credal sets, algebraic model counting

TL;DR: We propose to perform inference in probabilistic answer set programs with imprecise probabilities, represented as credal facts and credal annotated disjunctions, via optimization.

Abstract:

Probabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions.
This increases the expressivity of the language but, at the same time, the cost of inference.
In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation.
Empirical results on different datasets with multiple configurations shows the effectiveness of our approach.

List Of Authors: Azzolini, Damiano and Riguzzi, Fabrizio

Code Url: https://github.com/damianoazzolini/aspmc

Submission Number: 42