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Answering the “why” in answer set programming – A survey of explanation approaches

Published online by Cambridge University Press:  15 January 2019

JORGE FANDINNO Open the ORCID record for JORGE FANDINNO [Opens in a new window]  and
CLAUDIA SCHULZ
JORGE FANDINNO
Affiliation:
Institut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse, France (e-mail: [email protected])
CLAUDIA SCHULZ
Affiliation:
Ubiquitous Knowledge Processing (UKP) Lab, Technische Universität Darmstadt, Darmstadt, Germany (e-mail: [email protected])
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Abstract

Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union’s new General Data Protection Regulation tries to tackle this problem by stipulating a “right to explanation” for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is answer set programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations, and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.

Keywords

answer setexplanationjustificationdebugging
Type
Survey Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 2: User-Oriented Logic Programming and Reasoning Paradigms , March 2019 , pp. 114 - 203
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

We are thankful to the anonymous reviewers for their valuable feedback, which helped to improve the paper. This study was funded by Centre International de Mathématiques et d’Informatique de Toulouse ANR-11-LABEX-0040-CIMI.

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