Some experiments with a hybrid model for learning sequential decision making
To deal with sequential decision tasks, we present a learning model CLARION, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in our earlier work (Artificial Intelligence, 75(2) (1995) 241-296). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies 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. (C) 1998 Elsevier Science Inc. All rights reserved.
hybrid models sequential decision making connectionist models declarative knowledge reinforcement learning cognitive models procedural knowledge