A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction

Yaqing Hou, Yifeng Zeng, Yew Soon Ong

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    Abstract

    Memetic Multi-Agent System (MeMAS) emerges as an en- hanced version of multi-agent systems with the implementa- tion of meme-inspired agents. Previous research of MeMAS has developed a computational framework in which a series of memetic operations have been designed for implementing multiple interacting agents. This paper further endeavors to address the speci c challenges that arise in more com- plex multi-agent settings where agents share a common set- ting with other agents who have di erent and even compet- itive objectives. Particularly, we propose a memetic multi- agent demonstration learning approach (MeMAS-P) with improvement over existing work to allow agents to improve their performance by building candidate models and accord- ingly predicting behaviors of their opponents. Experiments based on an adapted mine eld navigation task have shown that MeMAS-P could provide agents with ability to acquire increasing level of learning capability and reduce the candi- date model space by sharing meme-inspired demonstrations with respect to their representative knowledge and unique candidate models.
    Original languageEnglish
    Title of host publicationProceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems
    EditorsCatholijn M. Jonker, Stacy Marsella, John Thaangarajah, Karl Tuyls
    PublisherACM
    Pages539-547
    ISBN (Electronic)9781450342391
    Publication statusPublished - 9 May 2016
    Event15th International Conference on Autonomous Agents and Multiagent Systems - Grand Copthorne Waterfront Hotel, Singapore
    Duration: 9 May 201613 May 2016
    Conference number: 15

    Conference

    Conference15th International Conference on Autonomous Agents and Multiagent Systems
    Abbreviated titleAAMAS2016
    CountrySingapore
    Period9/05/1613/05/16

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  • Cite this

    Hou, Y., Zeng, Y., & Ong, Y. S. (2016). A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction. In C. M. Jonker, S. Marsella, J. Thaangarajah, & K. Tuyls (Eds.), Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (pp. 539-547). ACM. https://dl.acm.org/citation.cfm?id=2937003