Learning as Goal-Driven Inference

Developing an adequate and general computational model of adaptive, multistrategy, and goal-oriented learning is a fundamental long-term objective for machine learning research for both theoretical and pragmatic reasons. We outline a proposal for developing such a model based on two key ideas. First, we view learning as an active process involving the formulation of learning goals during the performance of a reasoning task, the prioritization of learning goals, and the pursuit of learning goals using multiple learning strategies. The second key idea is to model learning as a kind of inference in which the system augments and reformulates its knowledge using various types of primitive inferential actions, known as knowledge transmutations.

Read the paper:

Learning as Goal-Driven Inference

by Ryszard Michalski, Ashwin Ram

In A. Ram & D. Leake (eds.), Goal-Driven Learning, chapter 21, MIT Press/Bradford Books, 1995

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