Technical Report
 

A knowledge level analysis of learning programs

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https://ir.library.oregonstate.edu/concern/technical_reports/s7526m31t

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  • This chapter develops a taxonomy of learning methods using techniques based on Newell’s knowledge level. Two properties of each system are defined: knowl­edge level predictability and knowledge level learning. A system is predictable at the knowledge level if the principle of rationality can be applied to predict its behavior. A system learns at the knowledge level if its knowledge level de­scription changes over time. These two definitions can be used to generate the three-class taxonomy. The taxonomy formalizes the intuition that there are two kinds of learning systems: systems that simply improve their efficiency (symbol-level learning SLL) and systems that acquire new knowledge (knowledge-level learning; KLL). The implications of the taxonomy for learning research are explored. Automatic programming research can provide ideas for SLL. Devel­opment of methods for KLL must rely either on the development of a principle of plausible rationality or OIL the construction of learning methods that work well only for certain kinds of environments. Explanation-based generalzation and chunking methods address only SLL and do not provide solutions to the problems of KLL.
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