Abstract
For dealing with reactive sequential decision tasks, a learning model Clarion was developed, which is a hybrid connectionist model consisting of both localist (symbolic) and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line, bottom-up learning (from subsymbolic to symbolic knowledge). Experiments in various situations shed light on the working of the model. Its theoretical implications in terms of symbol grounding are also discussed.
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Sun, R., Peterson, T. (2001). A Subsymbolic and Symbolic Model for Learning Sequential Decision Tasks. In: Furuhashi, T., Tano, S., Jacobsen, HA. (eds) Deep Fusion of Computational and Symbolic Processing. Studies in Fuzziness and Soft Computing, vol 59. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1837-6_1
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DOI: https://doi.org/10.1007/978-3-7908-1837-6_1
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