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Modélisation logique du raisonnement et de l’apprentissage : une approche bio-inspirée.

Abstract : In this dissertation, we take inspiration in cognitive sciences to address the issue of the logical modelling of reasoning and learning. Our main thrust is that to address these issues one should take inspiration in the way natural agents (i.e., humans and animals) actually proceed when they draw inferences and learn. Considering that reasoning incorporates a wide range of cognitive abilities, and that it would thus be unreasonable to hope to model the whole of human’s reasoning all at once, we focus here on a very basic kind of inferences that, we argue, can be considered as the primary core of reasoning in all brained animals. We identify a plausible underlying process for these inferences, first at the mental level of description and then at the neural level, and we develop a family of logical models that allow to simulate it. Then we tackle the issue of providing sets of rules to characterise the inference relations induced by these models. These rules are a by-product of the posited process, and should thus be seen as rules that, according to the model, result from the very functioning of brains. Finally we examine the learning processes attached to the considered inferences, and we show how to they can be modelled within our framework. To conclude we briefly discuss possible further developments of the framework, and in particular we give indications about how the modelling of some other cognitive abilities might be envisioned.
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Christel Grimaud. Modélisation logique du raisonnement et de l’apprentissage : une approche bio-inspirée.. Philosophie. Université Charles de Gaulle - Lille III, 2016. Français. ⟨NNT : 2016LIL30026⟩. ⟨tel-01425354⟩

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