Year: 2001
Type: article
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.
Source: Cognitive Science
Authors: R SUN, E MERRILL, T PETERSON
Institutions: University of Missouri, University of Alabama
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W2160395727