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Introspective multistrategy learning: On the construction of learning strategies by Michael T. Cox; Ashwin Ram is a Computer Science article available to read on EtoBox.

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A central problem in multistrategy learning systems is the selection and sequencing of machine learning algorithms for particular situations. This is typically done by the system designer who analyzes the learning task and implements the appropriate algorithm or sequence of algorithms for that task. We propose a solution to this problem which enables an AI system with a library of machine learning algorithms to select and sequence appropriate algorithms autonomously. Furthermore, instead of relying on the system designer or user to provide a learning goal or target concept to the learning system, our method enables the system to determine its learning goals based on analysis of its successes and failures at the performance task. The method involves three steps: Given a performance failure, the learner examines a trace of its reasoning prior to the failure to diagnose what went wrong (blame assignment); given the resultant explanation of the reasoning failure, the learner posts explicitly represented learning goals to change its background knowledge (deciding what to learn); and given a set of learning goals, the learner uses nonlinear planning techniques to assemble a sequence of m

Who reads Introspective multistrategy learning: On the construction of learning strategies?

Author

Michael T. Cox; Ashwin Ram

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0004-3702)

Published

1999

Language

EN

Field

Computer Science (Physical Sciences)