Answering the “why” in answer set programming – A survey of explanation approaches
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 explanat...
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| Veröffentlicht in: | Theory and practice of logic programming Jg. 19; H. 2; S. 114 - 203 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Cambridge, UK
Cambridge University Press
01.03.2019
Cambridge University Press (CUP) |
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| ISSN: | 1471-0684, 1475-3081 |
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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. |
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| AbstractList | 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. 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. |
| Author | SCHULZ, CLAUDIA FANDINNO, JORGE |
| Author_xml | – sequence: 1 givenname: JORGE orcidid: 0000-0002-3917-8717 surname: FANDINNO fullname: FANDINNO, JORGE email: jorge.fandinno@irit.fr organization: Institut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse, France (e-mail: jorge.fandinno@irit.fr) – sequence: 2 givenname: CLAUDIA surname: SCHULZ fullname: SCHULZ, CLAUDIA email: schulz@ukp.informatik.tu-darmstadt.de organization: Ubiquitous Knowledge Processing (UKP) Lab, Technische Universität Darmstadt, Darmstadt, Germany (e-mail: schulz@ukp.informatik.tu-darmstadt.de) |
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| Keywords | answer set debugging justification explanation Justification Answer set Explanation Debugging |
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| SubjectTerms | Artificial Intelligence Computer Science Cryptography Decision making Domains Machine Learning Mathematical programming Problem solving Solvers Special Issue on User-Oriented Logic Programming and Reasoning Paradigms Survey Article |
| Title | Answering the “why” in answer set programming – A survey of explanation approaches |
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