Learning in reactive sequential decision tasks: the CLARION model

In order to develop versatile agents that learn in situated contexts and generalize resulting knowledge to different environments, we explore the possibility of learning both procedural and declarative knowledge in a hybrid connectionist architecture. The architecture, CLARION, is based on the two-level idea proposed earlier by the authors. The architecture integrates reactive routines, rules, learning, and decision-making in a unified framework, and structures different learning components synergistically.