Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems

George A. Vilches; Annie S. Wu; John Sciortino; Daniel Pack; Jeffrey P. Ridder

doi:10.1117/12.719281

4 May 2007 Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems

George A. Vilches, Annie S. Wu, John Sciortino, Daniel Pack, Jeffrey P. Ridder

Proceedings Volume 6563, Evolutionary and Bio-inspired Computation: Theory and Applications; 656303 (2007) https://doi.org/10.1117/12.719281
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

George A. Vilches, Annie S. Wu, John Sciortino, Daniel Pack, and Jeffrey P. Ridder "Classifying and evolving multi-agent behaviors from animal packs in search and tracking problems", Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 656303 (4 May 2007); https://doi.org/10.1117/12.719281

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available