Skill Learning Using A Bottom-Up Hybrid Model


Abstract
top-down approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward lowlevel skill learning, where procedural knowledge develops rst and declarative knowledge develops from it. Clarionwhich follows this approach is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a mine eld navigation task. A match between the model and human data is observed in several comparisons.
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