Error Tolerant Plan Recognition: An Empirical Investigation

Abstract

Few plan recognition algorithms are designed to tolerate input errors. We describe a case-based plan recognition algorithm (SET-PR) that is robust to two input error types: missing and noisy actions. We extend our earlier work on SET-PR with more extensive evaluations by testing the utility of its novel action-sequence representation for plans and also investigate other design decisions (e.g., choice of similarity metric). We found that SET-PR outperformed a baseline algorithm for its ability to tolerate input errors, and that storing and leveraging state information in its plan representation substantially increases its performance.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2015
Accession Number
ADA623033

Entities

People

  • David W. Aha
  • Michael W. Floyd
  • Swaroop S. Vattam

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Coding
  • Convergence
  • Human-Robot Interaction
  • Information Operations
  • Intelligent Agents
  • Intelligent Systems
  • Kernel Functions
  • Mathematics
  • Military Research
  • Models
  • Pattern Recognition
  • Pilot Studies
  • Precision
  • Recognition
  • Sequences

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Artificial Intelligence
  • Speech Processing/Speech Recognition.