A Multistrategy Learning Scheme for Assimilating Advice in Embedded Agents

Abstract

The problem of designing and refining task-level strategies in an embedded multiagent setting is an important unsolved question. To address this problem, we have developed a multistrategy system that combines two learning methods: operationalization of high-level advice provided by a human and incremental refinement by a genetic algorithm. The first method generates seed rules for finer-grained refinements by the genetic algorithm. Our multistrategy learning system is evaluated on two complex simulated domains as well as with a Nomad 200 robot. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1993
Accession Number
ADA294087

Entities

People

  • Diana F. Gordon

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automatic Programming
  • Compilers
  • Computer Programming
  • Control Theory
  • Differential Equations
  • Equations
  • Game Theory
  • Genetic Algorithms
  • Genetics
  • Machine Learning
  • Navigation
  • Neural Networks
  • Population Genetics
  • Reinforcement Learning
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Biotechnology