Lamarckian Learning in Multi-Agent Environments.

Abstract

Genetic algorithms gain much of their power from mechanisms derived from the field of population genetics. However, it is possible, and in some cases desirable, to augment the standard mechanisms with additional features not available in biological systems. In this paper, we examine the use of Lamarckian learning operators in the SAMUEL architecture. The use of the operators is illustrated on three tasks in multi-agent environments. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1995
Accession Number
ADA294084

Entities

People

  • John J. Grefenstette

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Control Knobs
  • Demographic Cohorts
  • Detectors
  • Environment
  • Genetic Algorithms
  • Hierarchies
  • Language
  • Learning
  • Machine Learning
  • Mutations
  • Natural Languages
  • Population Genetics
  • Probability
  • Random Walk
  • Systems Biology

Readers

  • Molecular Genetics
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Biotechnology