Skill Learning Using A Bottom-Up Hybrid Model
نویسندگان
چکیده
This paper presents a skill learning model Clarion. Diierent from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops rst and declarative knowledge develops from it. Clarion which follows this approach is formed by integrating connec-tionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a mineeeld navigation task. A match between the model and human data is observed in several comparisons.
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