Spatio-Temporal Case-Based Reasoning for Behavioral Selection

Abstract

This paper presents the application of a Case-Based Reasoning (CBR) approach to the selection and modification of behavioral assemblage parameters. The goal of this research is to achieve an optimal parameterization of robotic behaviors in run-time. This increases robot performance and makes a manual configuration of parameters unnecessary. The CBR module selects a set of parameters for an active behavioral assemblage in real-time. This set of parameters fits the environment better than hand-coded ones, and its performance is monitored providing feedback for a possible re-selection of the parameters. This paper places a significant emphasis on the technical details of the CBR module and how it is integrated within a schema-based reactive navigation system. The paper also presents the results and evaluation of the system in both simulated and real-world robotic experiments.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA442295

Entities

People

  • Maxim Likhachev
  • Ronald C. Arkin

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computations
  • Environment
  • Identification
  • Index Terms
  • Information Operations
  • Learning
  • Motion Planning
  • Reasoning
  • Relative Motion
  • Robotics
  • Robots
  • Simulations
  • Simulators
  • Statistical Data
  • Switching

Fields of Study

  • Computer science
  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy