Knowledge-directed learning

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Abstract
A system embodying a knowledge-directed approach to unsupervised learning is examined in this paper. This approach is based on the premise that knowledge of new situations is acquired and interpreted in terms of the previous knowledge brought to the learning situation. In particular, our system is provided with a general characterization of action-oriented competitive games. This frame of reference is used to construct an interpretation for the patterns of human activity that are observed in games of baseball.Multiple levels of knowledge and processing are used to proceed through various levels of description of the observed human behavior. Hypothesis Generation shifts the pattern description from observed physical actions such as "catch" and "run" to inferred goals and causal relationships of the players executing those actions. Hypothesis Generalization abstracts generalized classes of events and schemata that represent concepts such as "hit" and "out". Hypothesis Evaluation closes the loop in the learning process by verifying or rejecting the various hypotheses. Knowledge encoded as schemata direct these processes; there are schemata for inferring competitive and cooperative goals and causal relationships of players.An important aspect of the system is its ability to use acquired knowledge. The multi-level organization facilitates the integration of the new information into the existing knowledge structure. Also, both the initial knowledge and the acquired knowledge are represented uniformly as schemata (production rules). Acquired schemata, then, are available to assist in interpreting and predicting future events. This ability demonstrates the effectiveness of our knowledge-directed approach to learning.

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