Explainable Answer-set Programming
- URL: http://arxiv.org/abs/2308.15901v1
- Date: Wed, 30 Aug 2023 09:09:57 GMT
- Title: Explainable Answer-set Programming
- Authors: Tobias Geibinger (TU Wien)
- Abstract summary: Project aims to fill some of these gaps and contribute to the state of the art in explainable ASP.
We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The interest in explainability in artificial intelligence (AI) is growing
vastly due to the near ubiquitous state of AI in our lives and the increasing
complexity of AI systems. Answer-set Programming (ASP) is used in many areas,
among them are industrial optimisation, knowledge management or life sciences,
and thus of great interest in the context of explainability. To ensure the
successful application of ASP as a problem-solving paradigm in the future, it
is thus crucial to investigate explanations for ASP solutions. Such an
explanation generally tries to give an answer to the question of why something
is, respectively is not, part of the decision produced or solution to the
formulated problem. Although several explanation approaches for ASP exist,
almost all of them lack support for certain language features that are used in
practice. Most notably, this encompasses the various ASP extensions that have
been developed in the recent years to enable reasoning over theories, external
computations, or neural networks. This project aims to fill some of these gaps
and contribute to the state of the art in explainable ASP. We tackle this by
extending the language support of existing approaches but also by the
development of novel explanation formalisms, like contrastive explanations.
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