Case-Based Reasoning in Transfer Learning

Abstract

Positive transfer learning (TL) occurs when, after gaining experience from learning how to solve a (source) task, the same learner can exploit this experience to improve performance and/or learning on a different (target) task. TL methods are typically complex, and case-based reasoning can support them in multiple ways. We introduce a method for recognizing intent in a source task, and then applying that knowledge to improve the performance of a case-based reinforcement learner in a target task. We report on its ability to significantly outperform baseline approaches for a control task in a simulated game of American football. We also compare our approach to an alternative approach where source and target task learning occur concurrently, and discuss the tradeoffs between them.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA554949

Entities

People

  • David W. Aha
  • Gita Sukthankar
  • Matthew Molineaux

Organizations

  • Knexus Research (United States)

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Bayesian Networks
  • Computational Science
  • Computer Science
  • Computers
  • Engineering
  • Hidden Markov Models
  • Kernel Functions
  • Machine Learning
  • Probability
  • Psychology
  • Reasoning
  • Reinforcement Learning
  • Simulators
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Instructional Design and Training Evaluation.
  • Manufacturing Engineering.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.