By Sofia Ceppi, Nicola Gatti, Claudio Iuliano (auth.), Esther David, Valentin Robu, Onn Shehory, Sebastian Stein, Andreas Symeonidis (eds.)
This quantity includes ten completely refereed and revised papers detailing contemporary advances in study on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially offered on the thirteenth overseas Workshop on Agent-Mediated digital trade (AMEC 2011), collocated with AAMAS 2011 in Taipei, Taiwan, or on the 2011 Workshop on buying and selling Agent layout and research (TADA 2011), collocated with IJCAI 2011 in Barcelona, Spain.
The papers offered at those workshops illustrate either the intensity and vast diversity of study issues during this box. they vary from offering recommendations to open theoretical difficulties in on-line scheduling and bargaining below uncertainty, to designing bidding brokers in a large zone of software components, comparable to digital trade, provide chain administration, or key-phrase ads, to designing brokers which could effectively mirror real human behaviors in real looking games.
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Additional resources for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC 2011, Taipei, Taiwan, May 2, 2011, and TADA 2011, Barcelona, Spain, July 17, 2011, Revised Selected Papers
In Section 3, our data extraction and prediction methodology is outlined including how the feature vector is augmented to include data from past observations. Our prediction results are presented and compared with the state-of-the-art methods in Section 5. Section 6 concludes with a discussion of future work. 2 Background and Related Work The majority of prediction models can be classified as one of two types: on-line (only information from a short window of recent observations is used) and off-line (significant volumes of historical data are used to calibrate the model).
For this reason our model incorporates price observations for multiple component lead times. Second, the model does not address trends that can be anticipated by observing changes in price over time. Observations of prices of an individual product over several past time steps (pt ,pt−1 ,pt−2 ,. . ) provide information to form a prediction about the next product price (pt+1 ). This is equivalent to a univariate time series prediction. Information from previous time steps is included in our model.
Finally, it would be desirable to identify key parameters that influence more or less the optimal bidding percentage. References 1. S. Internet Advertising Revenue Report for 2009 (April 2010) 2. : Sponsored search: an overview of the concept, history, and technology. International Journal of Electronic Business 6(2), 114–131 (2008) 3. : The ad auctions game for the 2010 trading agent competition. Technical report, University of Michigan, Ann Arbor, MI 48109-2121 USA (2010) 4. : A Game Theoretic Bidding Agent for the Ad Auction Game.