Input understanding as a basis for multistrategy task-adaptive learning
The paper explores several general issues in developing a multistrategy task-adaptive learning (MTL) system. The system aims at integrating a whole range of learning strategies, such as explanation-based learning, empirical generalization, abduction, constructive induction, learning by analogy and abstraction. The integration is dynamic, i.e. the way different strategies are evoked depends on the learning task at hand. The key idea of the learning method is that the learner tries to “understand” the input in terms of its current knowledge, and then uses this understanding to improve the knowledge. This process may involve both certain and plausible reasoning. The paper extends and generalizes the previous work on this topic.
Keywordsmultistrategy learning induction analogy abduction abstraction explanation-based learning knowledge acquisition
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