Abstract
AbstractThis paper presents a skill learning model CLARION. Different from existing models of mostly high‐level skill learning that use a top‐down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom‐up approach toward low‐level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on‐line reactive learning. It adopts a two‐level dual‐representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.
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