Explanations as Programs in Probabilistic Logic Programming

TL;DR

This paper introduces a novel method for generating explanations in probabilistic logic programming by transforming queries into programs that explicitly show inference chains, making explanations minimal, customizable, and more interpretable.

Contribution

It proposes a new approach where explanations are represented as programs derived from queries, explicitly revealing inference chains in probabilistic logic programming.

Findings

Generated explanations are minimal and relevant.

Explanations can be customized by hiding uninteresting details.

The method clarifies the inference process for probabilistic logic queries.

Abstract

The generation of comprehensible explanations is an essential feature of modern artificial intelligence systems. In this work, we consider probabilistic logic programming, an extension of logic programming which can be useful to model domains with relational structure and uncertainty. Essentially, a program specifies a probability distribution over possible worlds (i.e., sets of facts). The notion of explanation is typically associated with that of a world, so that one often looks for the most probable world as well as for the worlds where the query is true. Unfortunately, such explanations exhibit no causal structure. In particular, the chain of inferences required for a specific prediction (represented by a query) is not shown. In this paper, we propose a novel approach where explanations are represented as programs that are generated from a given query by a number of unfolding-like transformations. Here, the chain of inferences that proves a given query is made explicit. Furthermore, the generated explanations are minimal (i.e., contain no irrelevant information) and can be parameterized w.r.t. a specification of visible predicates, so that the user may hide uninteresting details from explanations.