A Declarative Approach to Bias in Concept Learning

Stuart J. Russell, Benjamin N. Grosof

We give a declarative formulation of the biases used in inductive concept learning, particularly the Version-Space approach. We then show how the process of learning a concept from examples can be implemented as a first-order deduction from the bias and the facts describing the instances. This has the following advantages: 1) multiple sources and forms of knowledge can be incorporated into the learning process; 2) the learning system can be more fully integrated with the rest of the beliefs and reasoning of a complete intelligent agent. Without a semantics for the bias, we cannot generally and practically build machines that generate inductive biases automatically and hence are able to learn independently. With this in mind, we show how one part of the bias for Meta-DENDRAL, its instance description language, can be represented using first-order axioms called determinations, and can be derived from basic background knowledge about chemistry. The second part of the paper shows how bias can be represented as defaults, allowing shift of bias to be accommodated in a nonmonotonic framework.

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