Memetic multiagent 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 human-like 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.
|Number of pages||8|
|Publication status||Published - 2013|
|Event||IEEE Congress on Evolutionary Computation - Fiesta Americana Grand Coral Beach Hotel, Cancun, Mexico|
Duration: 20 Jun 2013 → 23 Jun 2013
|Conference||IEEE Congress on Evolutionary Computation|
|Period||20/06/13 → 23/06/13|
Chen, X., Zeng, Y., Ong, Y-S., Sing Ho, C., & Xiang, Y. (2013). A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems. Paper presented at IEEE Congress on Evolutionary Computation, Cancun, Mexico.