Reasoning and Learning About Past Temporal Knowledge in Connectionist Models

The integration of logic-based inference systems and connectionist learning architectures may lead to the construction of semantically sound cognitive models in artificial intelligence. The use of hybrid systems has shown promising results as regards the computation and learning of classical reasoning within neural networks. However, there still remains a number of open research issues on the integration of non-classical logics and neural networks. We present a new model for integrating symbolic reasoning about past temporal information and neural learning systems. We propose algorithms that translate background knowledge into a neural network and analyse the effectiveness of learning algorithms when subject to symbolic temporal knowledge. This opens several interesting research paths with possible applications to agents' decision making, cognitive modelling and knowledge-based systems.