Paper Info
Title
A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems
Abstract
Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557757
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
evolutionary computation,like-attracts-like principles,memetic operations,meme-inspired computational agents,multi-agent systems,memetic multiagent systems,memetic automaton,elitist selection criterion,towards human-like behavior,multiagent systems,human-like social behavior,complex dynamic environment,universal darwinism,multi agent systems,vectors,entropy
Memetic algorithm,Computer science,Universal Darwinism,Evolutionary computation,Multi-agent system,Selection criterion,Artificial intelligence,Machine learning
Conference
Volume
Issue
ISBN
null
null
978-1-4799-0452-5
Citations 
PageRank 
References 
2
0.38
18
Authors
5
Name
Order
Citations
PageRank
Xuefeng Chen1394.55
Yifeng Zeng241543.27
Yew-Soon Ong34205224.11
Choon Sing Ho491.57
Yanping Xiang515721.73