A hybrid model for learning sequential navigation

To deal with reactive sequential decision tasks, we present a learning model Clar-ion, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It uniies neural, reinforcement , and symbolic methods to perform on-line, bottom-up learning. Experiments in various situations are reported that shed light on the working of the model.