Structured Memetic Automation for Online Human-like Social Behavior Learning

Yifeng Zeng; Xufeng Chen; Yew Soon Ong; Jing Tang; Yanping Xiang

doi:10.1109/TEVC.2016.2577593

Structured Memetic Automation for Online Human-like Social Behavior Learning

Yifeng Zeng, Xufeng Chen, Yew Soon Ong, Jing Tang, Yanping Xiang

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Abstract

Meme automaton is an adaptive entity that autonomously acquires increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper embarks a study on memetic multiagent system (MeMAS) towards 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 the present 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 performance of the proposed MeMAS on human-like social behavior.

Original language	English
Pages (from-to)	-
Journal	IEEE Transactions on Evolutionary Computation
DOIs	https://doi.org/10.1109/TEVC.2016.2577593
Publication status	Published - 1 Feb 2017

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title = "Structured Memetic Automation for Online Human-like Social Behavior Learning",

abstract = "Meme automaton is an adaptive entity that autonomously acquires increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper embarks a study on memetic multiagent system (MeMAS) towards human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin{\textquoteright}s theory of natural selection and Dawkins{\textquoteright} notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents{\textquoteright} mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in the present 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 performance of the proposed MeMAS on human-like social behavior.",

author = "Yifeng Zeng and Xufeng Chen and Ong, {Yew Soon} and Jing Tang and Yanping Xiang",

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AU - Zeng, Yifeng

AU - Chen, Xufeng

AU - Ong, Yew Soon

AU - Tang, Jing

AU - Xiang, Yanping

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N2 - Meme automaton is an adaptive entity that autonomously acquires increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper embarks a study on memetic multiagent system (MeMAS) towards 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 the present 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 performance of the proposed MeMAS on human-like social behavior.

AB - Meme automaton is an adaptive entity that autonomously acquires increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper embarks a study on memetic multiagent system (MeMAS) towards 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 the present 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 performance of the proposed MeMAS on human-like social behavior.

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Structured Memetic Automation for Online Human-like Social Behavior Learning

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