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Conference Papers Year : 2017

Self-Correcting Unsound Reasoning Agents (DARe 2017)


This paper introduces a formal framework for relating learning and deduction in reasoning agents. Our goal is to capture imperfect reasoning as well as the progress, through introspection, towards a better reasoning ability. We capture the interleaving between these by a reasoning/deduction connection and we show how this—and related—definition apply to a setting in which agents are modeled by first-order logic theories. In this setting, we give a sufficient condition on the connection ensuring that under fairness assumptions the limit of introspection steps is a sound and complete deduction system. Under the same assumption we prove every falsehood is eventually refuted, hence the self-correction property.
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hal-03128332 , version 1 (04-02-2021)


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  • HAL Id : hal-03128332 , version 1


Yannick Chevalier. Self-Correcting Unsound Reasoning Agents (DARe 2017). 4th International Workshop on Defeasible and Ampliative Reasoning (DARe 2017), Jul 2017, Espoo, Finland. pp.16-28. ⟨hal-03128332⟩
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