Space of Reasons and Mathematical Model
This tackles a foundational problem in philosophy and AI for understanding concept use, but appears incremental as it builds on existing ideas.
The paper addresses how to represent the conditionality of language use in models, proposing a method to represent implications of propositional logic and conceptual determinations in a neural network model.
Inferential relations govern our concept use. In order to understand a concept it has to be located in a space of implications. There are different kinds of conditions for statements, i.e. that the conditions represent different kinds of explanations, e.g. causal or conceptual explanations. The crucial questions is: How can the conditionality of language use be represented. The conceptual background of representation in models is discussed and in the end I propose how implications of propositional logic and conceptual determinations can be represented in a model of a neural network.