Meme selection, variation, and transmission in multi-agent system
The incorporation of intelligent agents into the realms of Virtual Environments (VE) has demonstrated a high potential in improving user interactivity, participation and satisfaction in these virtual worlds. In order to stay relevant to the demands and expectations of users in a Virtual Environment,...
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sg-smu-ink.sis_research-76852022-01-13T05:30:03Z Meme selection, variation, and transmission in multi-agent system PAN, Zheng-Xiang FENG, Liang ONG, Yew-Soon KANG, Yilin TAN, Ah-hwee MIAO, Chunyan The incorporation of intelligent agents into the realms of Virtual Environments (VE) has demonstrated a high potential in improving user interactivity, participation and satisfaction in these virtual worlds. In order to stay relevant to the demands and expectations of users in a Virtual Environment, agents must be able to adapt quickly and learn from other agents in order to perform well. The novelty of this paper lies in leveraging the potential of a new perspective of memetic computing and cultural transmission in a multi-agent environment through cultural evolution. This paper shares the concept from the perspective of not only vertically but also horizontal transmission of knowledge through memes selection, variation and transmission which calls for empirically verifying whether such a paradigm is viable for improving agents' learning capabilities in a multiagent environment. This research seeks to use the notion of memes in order to equip virtual agents with the essential learning capabilities that are closer to humanity. 2010-09-23T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6682 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Multi-Agent Memetic Culture Evolution Artificial Intelligence and Robotics Graphics and Human Computer Interfaces |
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Multi-Agent Memetic Culture Evolution Artificial Intelligence and Robotics Graphics and Human Computer Interfaces PAN, Zheng-Xiang FENG, Liang ONG, Yew-Soon KANG, Yilin TAN, Ah-hwee MIAO, Chunyan Meme selection, variation, and transmission in multi-agent system |
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The incorporation of intelligent agents into the realms of Virtual Environments (VE) has demonstrated a high potential in improving user interactivity, participation and satisfaction in these virtual worlds. In order to stay relevant to the demands and expectations of users in a Virtual Environment, agents must be able to adapt quickly and learn from other agents in order to perform well. The novelty of this paper lies in leveraging the potential of a new perspective of memetic computing and cultural transmission in a multi-agent environment through cultural evolution. This paper shares the concept from the perspective of not only vertically but also horizontal transmission of knowledge through memes selection, variation and transmission which calls for empirically verifying whether such a paradigm is viable for improving agents' learning capabilities in a multiagent environment. This research seeks to use the notion of memes in order to equip virtual agents with the essential learning capabilities that are closer to humanity. |
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text |
author |
PAN, Zheng-Xiang FENG, Liang ONG, Yew-Soon KANG, Yilin TAN, Ah-hwee MIAO, Chunyan |
author_facet |
PAN, Zheng-Xiang FENG, Liang ONG, Yew-Soon KANG, Yilin TAN, Ah-hwee MIAO, Chunyan |
author_sort |
PAN, Zheng-Xiang |
title |
Meme selection, variation, and transmission in multi-agent system |
title_short |
Meme selection, variation, and transmission in multi-agent system |
title_full |
Meme selection, variation, and transmission in multi-agent system |
title_fullStr |
Meme selection, variation, and transmission in multi-agent system |
title_full_unstemmed |
Meme selection, variation, and transmission in multi-agent system |
title_sort |
meme selection, variation, and transmission in multi-agent system |
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Institutional Knowledge at Singapore Management University |
publishDate |
2010 |
url |
https://ink.library.smu.edu.sg/sis_research/6682 |
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1770576023243456512 |