Toward a Unified Approach for Conceptual Knowledge Acquisition
In keeping with a desire to abstract general principles in AI, this article begins to examine some relationships among heuristic learning in search, classification of utility, properties of certain structures, measurement of acquired knowledge, and efficiency of associated learning. In the process, a simple definition is given for conceptual knowledge, considered as information compression. The discussion concludes that domain-specific conceptual knowledge can be acquired. Among other implications of the analysis is that statistical observation of probabilities can result in the equivalent of planning, in low susceptibility to error, and in efficient learning.
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