Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83441
Title: Structured Memetic Automation for Online Human-Like Social Behavior Learning
Authors: Zeng, Yifeng
Chen, Xuefeng
Ong, Yew-Soon
Tang, Jing
Xiang, Yanping
Keywords: Human-like behavior
Memetic automaton
Issue Date: 2017
Source: Zeng, Y., Chen, X., Ong, Y.-S., Tang, J., & Xiang, Y. (2017). Structured Memetic Automation for Online Human-Like Social Behavior Learning. IEEE Transactions on Evolutionary Computation, 21(1), 102-115.
Series/Report no.: IEEE Transactions on Evolutionary Computation
Abstract: Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior.
URI: https://hdl.handle.net/10356/83441
http://hdl.handle.net/10220/42586
ISSN: 1089-778X
DOI: http://dx.doi.org/10.1109/TEVC.2016.2577593
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TEVC.2016.2577593].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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