By Florin Constantin, David C. Parkes (auth.), John Collins, Peyman Faratin, Simon Parsons, Juan A. Rodriguez-Aguilar, Norman M. Sadeh, Onn Shehory, Elizabeth Sklar (eds.)
This publication constitutes the completely refereed post-conference lawsuits of the ninth overseas Workshop on Agent-Mediated digital trade, AMEC IX, co-located with the 6th foreign Joint convention on self sufficient brokers and Multiagent platforms, AAMAS 2007, held in Honolulu, Hawai, in may well 2007, and the fifth Workshop on buying and selling Agent layout and research, TADA 2007, co-located with the Twenty-Second AAAI convention on synthetic Intelligence, AAAI 2007, held in Vancouver, Canada, in July 2007.
This quantity provides 15 rigorously revised and chosen papers from those workshops. the first and complementary objective of either workshops used to be to proceed to collect novel paintings from different fields on modeling, implementation and evaluate of computational buying and selling associations and/or agent ideas. The papers originating from AMEC specialise in a wide number of matters on auctions, negotiation, and strategic habit in digital marketplaces. The papers originating from TADA replicate the hassle of the neighborhood to layout situations the place buying and selling agent designers and industry designers could be pitched opposed to one another.
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Additional info for Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis: AAMAS 2007 Workshop, AMEC 2007, Honolulu, Hawaii, May 14, 2007, and AAAI 2007 Workshop, TADA 2007, Vancouver, Canada, July 23, 2007, Selected and Revised Papers
2 At the time we designed our agent, only agents from the 2005 competition were available; however, in the experiments of this section, we make use of additional agents that have become available since then, including some of the agents that participated in the 2006 competition, as this allows us to present experiments involving a wider variety of agents. We chose four diﬀerent agent groupings, and ran 50 games with each group. The groups are shown in Table 3. The ﬁrst three groups contain TacTex-06 and ﬁfteen additional agents.
Acknowledgments We would like to thank the SICS team for developing the TAC SCM game server and all teams that have contributed to the agent repository. This research was supported in part by NSF CAREER award IIS-0237699 and NSF grant EIA0303609. References 1. : Opponent modeling in multi–agent systems. , Sen, S. ) IJCAI-WS 1995. LNCS, vol. 1042, pp. 40–52. Springer, Heidelberg (1996) 2. : Online learning about other agents in a dynamic multiagent system. , Wooldridge, M. ) Proceedings of the 2nd International Conference on Autonomous Agents, pp.
The results of both approaches are shown in Figure 5. We can see from the results that none of the approaches described in this section signiﬁcantly outperform the ﬁxed model for the ﬁrst four games, but that both the method of combining data and the method of combining new and old predictors outperform the ﬁxed and learned predictors when six or more games are available for training. The method of combining new and old predictors results in the lowest error, and this result is statistically signiﬁcant with at least 95% conﬁdence after at least six games have been played.
Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis: AAMAS 2007 Workshop, AMEC 2007, Honolulu, Hawaii, May 14, 2007, and AAAI 2007 Workshop, TADA 2007, Vancouver, Canada, July 23, 2007, Selected and Revised Papers by Florin Constantin, David C. Parkes (auth.), John Collins, Peyman Faratin, Simon Parsons, Juan A. Rodriguez-Aguilar, Norman M. Sadeh, Onn Shehory, Elizabeth Sklar (eds.)