A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction

Hdl Handle:
http://hdl.handle.net/10149/600591
Title:
A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction
Authors:
Hou, Y. (Yaqing); Zeng, Y. (Yifeng); Ong, Y. S. (Yew-Soon)
Affiliation:
Teesside University, Digital Futures Institute
Citation:
Hou, Yaqing; Zeng, Yifeng & Ong, Yew-Soon (2016) 'A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction' Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), 9-13 May 2016, Grand Copthorne Waterfront Hotel, Singapore
Publisher:
ACM
Journal:
Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems
Conference:
the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), 9-13 May 2016, Grand Copthorne Waterfront Hotel, Singapore
Issue Date:
9-May-2016
URI:
http://hdl.handle.net/10149/600591
Additional Links:
http://sis.smu.edu.sg/aamas2016?itemid=671
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.
Type:
Meetings and Proceedings
Language:
en
Rights:
ACM allows authors' version of their own ACM-copyrighted work on their personal server or on severs belonging to their employers. For full details see http://www.acm.org/publications/policies/RightsResponsibilities [Accessed 04/03/2016]

Full metadata record

DC FieldValue Language
dc.contributor.authorHou, Y. (Yaqing)en
dc.contributor.authorZeng, Y. (Yifeng)en
dc.contributor.authorOng, Y. S. (Yew-Soon)en
dc.date.accessioned2016-03-04T13:55:08Zen
dc.date.available2016-03-04T13:55:08Zen
dc.date.issued2016-05-09en
dc.identifier.urihttp://hdl.handle.net/10149/600591en
dc.description.abstractMemetic 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.en
dc.language.isoenen
dc.publisherACMen
dc.relation.urlhttp://sis.smu.edu.sg/aamas2016?itemid=671en
dc.rightsACM allows authors' version of their own ACM-copyrighted work on their personal server or on severs belonging to their employers. For full details see http://www.acm.org/publications/policies/RightsResponsibilities [Accessed 04/03/2016]en
dc.titleA Memetic Multi-Agent Demonstration Learning Approach with Behavior Predictionen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentTeesside University, Digital Futures Instituteen
dc.identifier.journalProceedings of the 15th International Conference on Autonomous Agents and Multiagent Systemsen
dc.identifier.conferencethe 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), 9-13 May 2016, Grand Copthorne Waterfront Hotel, Singaporeen
or.citation.harvardHou, Yaqing; Zeng, Yifeng & Ong, Yew-Soon (2016) 'A Memetic Multi-Agent Demonstration Learning Approach with Behavior Prediction' Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2016), 9-13 May 2016, Grand Copthorne Waterfront Hotel, Singaporeen
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