# Explanations as Programs in Probabilistic Logic Programming

arXiv: https://arxiv.org/abs/2210.03021 · HTML: https://mlsanity.com/paper/2210.03021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [Justifications for Goal-Directed Constraint Answer Set Programming](https://mlsanity.com/paper/2009.10238/) (2020)
- [A System for Explainable Answer Set Programming](https://mlsanity.com/paper/2009.10242/) (2020)
- [Syntactic Requirements for Well-defined Hybrid Probabilistic Logic Programs](https://mlsanity.com/paper/2109.08283/) (2021)

## Built on by (most cited first, 2 of 2)

- [Revisiting Rogers' Paradox in the Context of Human-AI Interaction](https://mlsanity.com/paper/2501.10476/) (2025)
- [Explaining Explanations in Probabilistic Logic Programming](https://mlsanity.com/paper/2401.17045/) (2024)

## Abstract (arXiv)

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.