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, 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.