Invited Keynote Talk Modeling Reasoning Mechanisms by Neural-Symbolic Learning

Currently, neural-symbolic integration covers – at least in theory – a whole bunch of types of reasoning: neural representations (and partially also neural-inspired learning approaches) exist for modeling propositional logic (programs), whole classes of manyvalued logics, modal logic, temporal logic, and epistemic logic, just to mention some important examples [2,4]. Besides these propositional variants of logical theories, also first proposals exist for approximating “infinity” with neural means, in particular, theories of first-order logic. An example is the core method intended to learn the semantics of the single-step operator TP for first-order logic (programs) with a neural network [1]. Another example is the neural approximation of variable-free first-order logic by learning representations of arrow constructions (which represent logical expressions) in the R using Topos constructions [3].