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Collaborating and Learning Predators on a Pursuit Scenario

Veröffentlicht: September 2010
Herausgeber: Mike Hinchey, Bernd Kleinjohann, Lisa Kleinjohann, Peter Lindsay, Franz Rammig, Jon Timmis, and Marilyn Wolf
Buchtitel: Distributed, Parallel and Biologically Inspired Systems
Ausgabe: 329
Seiten: 290-301
Reihe: IFIP Advances in Information and Communication Technology
Verlag: Springer
Erscheinungsort / Ort: Boston
Bemerkung: 3rd IFIP Conference on Biologically-Inspired Collaborative Computing (BICC 2010)

A generic predator/prey pursuit scenario is used to validate a common learning approach using Wilson’s extended learning classifier system (XCS). The predators, having only local information, should independently learn and act while at the same time they are urged to collaborate and to capture the prey. Since learning from scratch is often a time consuming process, the common learning approach, as investigated here, is compared to an individual learning approach of selfish learning agents. A special focus is set on the performance of how quickly the team goal is achieved in both learning scenarios. This paper provides new insights of how agents with local information could learn collaboratively in a dynamically changing multi-agent environment. Furthermore, the concept of a common rule base based on Wilson's XCS is investigated. The results based on the common rule base approach show a significant speed up in the learning performance but may be significantly inferior on the long run, in particular in situations with a moving prey.

ISBN: 978-3-642-15233-7
DOI Link: 10.1007/978-3-642-15234-4_28


OCCS (Phase III)


Effiziente Algorithmen


Organic Computing, Maschinelles Lernen, Agentensysteme