|
Abstract
|
This 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. © 2001 Cognitive Science Society, Inc. All rights reserved.
|
|
Keywords
|
No keywords specified (fix it)
|
|
Categories
|
|
|
Options
|
|
| |
|
Download options
Try with proxy.
Similar books and articles
Analytics
My notes
Discussion
