Title

Classifying And Evolving Multi-Agent Behaviors From Animal Packs In Search And Tracking Problems

Keywords

Behavior classification; Genetic algorithms; Pack behaviors; Search and tracking algorithms

Abstract

This work investigates the efforts behind defining a classification system for multi-agent search and tracking problems, specifically those based on relatively small numbers of agents. The pack behavior search and tracking classification (PBSTC) we define as mappings to animal pack behaviors that regularly perform activities similar to search and tracking problems, categorizing small multi-agent problems based on these activities. From this, we use evolutionary computation to evolve goal priorities for a team of cooperating agents. Our goal priorities are trained to generate candidate parameter solutions for a search and tracking problem in an emitter/sensor scenario. We identify and isolate several classifiers from the evolved solutions and how they reflect on the agent control systems's ability in the simulation to solve a task subset of the search and tracking problem. We also isolate the types of goal vector parameters that contribute to these classified behaviors, and categorize the limitations from those parameters in these scenarios.

Publication Date

11-15-2007

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

6563

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.719281

Socpus ID

35948997431 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/35948997431

This document is currently not available here.

Share

COinS