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Combining expert-based beliefs and answer sets

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Abstract

Answer Set Programming (ASP) is a declarative knowledge representation language that uses a non-monotonic reasoning mechanism to search for all answer sets or models of a specific problem. This makes it suitable for problem-solving activities, such as expertise, where there is lack of knowledge, and where defeasible reasoning is required. However, this language is not equipped with a means to select a preferred model among its answer sets as done by experts in expertise processes. Clearly, in expertise processes, experts who have acquired knowledge from their experience will express possible explanations and based on their beliefs and reasoning, will select the most appropriate ones for the problem.

To have the best of both ASP and human expert knowledge in expertise process activities, we propose and illustrate a general and domain-independent framework that extends ASP using experts’ knowledge and belief functions to systematically draw explanations for expertise activities. This extension provides a means to evaluate ASP models’ beliefs using experts’ evidence distributions, while reducing the knowledge-intensive load of the expertise process.

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Authors and Affiliations

  1. Production Engineering Laboratory/UFTMiP, 47, Avenue d’Azereix, BP 1629, 65016, Tarbes Cedex, France

    Serge Sonfack Sounchio, Laurent Geneste & Bernard Kamsu Foguem

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  1. Serge Sonfack Sounchio
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  2. Laurent Geneste
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  3. Bernard Kamsu Foguem
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Correspondence to Laurent Geneste or Bernard Kamsu Foguem.

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Sounchio, S.S., Geneste, L. & Foguem, B.K. Combining expert-based beliefs and answer sets. Appl Intell 53, 2694–2705 (2023). https://doi.org/10.1007/s10489-022-03669-z

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