Introspective Multistrategy Learning: On the Construction of Learning Strategies

Abstract

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. The authors propose a solution to this problem that enables an Artificial Intelligence (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, this method enables the system to determine its own learning goals based on an 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 machine learning algorithms, represented as planning operators, to achieve the learning goals (learning-strategy construction). In support of these operations, the authors define the types of reasoning failures, a taxonomy of failure causes, a second-order formalism to represent reasoning traces, a taxonomy of learning goals that specify desired change to the background knowledge of a system, and a declarative task-formalism representation of learning algorithms. They present the Meta-AQUA system, an implemented multistrategy learner that operates in the domain of story understanding.

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Document Details

Document Type
Technical Report
Publication Date
Jun 08, 1999
Accession Number
ADA495230

Entities

People

  • Ashwin Ram
  • Michael T. Cox

Organizations

  • Wright State University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Application Software
  • Artificial Intelligence
  • Cognition
  • Computational Processes
  • Computer Programs
  • Computer Science
  • Computers
  • Construction
  • Damage Detection
  • Detection
  • Failure Mode And Effect Analysis
  • Intelligent Systems
  • Learning
  • Lisp Programming Language
  • Mental Processes
  • Standards

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks