Inference to the Stable Explanations
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Olivieri, Francesco
Rotolo, Antonino
Cristani, Matteo
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Gottlob, G
Inclezan, D
Maratea, M
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Italy, Genova
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Abstract
The process of explaining a piece of evidence by constructing a set of assumptions that are a good explanation for that evidence is ubiquitous in real-life (e.g. in legal systems). In this paper, we introduce, discuss, and formalise the notion of stable explanations in a non-monotonic setting. We show how, while applying it to the process of (1) computing a set of literals able to (2) derive a conclusion (3) from a set of defeasible rules, we obtain a restricted version of the notion of abduction. This is both interesting and useful: when an explanation for a given conclusion is stable, it can, in fact, be used to infer the same conclusion independently of other pieces of evidence that are found afterwards.
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LPNMR 2022: Logic Programming and Nonmonotonic Reasoning
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13416
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Subject
Logic
ABDUCTION
COMPLEXITY
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
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Governatori, G; Olivieri, F; Rotolo, A; Cristani, M, Inference to the Stable Explanations, LPNMR 2022: Logic Programming and Nonmonotonic Reasoning, 2022, 13416, pp. 245-258