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 fresh advances in learn on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially provided on the thirteenth foreign 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 awarded at those workshops illustrate either the intensity and huge variety of study themes during this box. they vary from offering strategies to open theoretical difficulties in on-line scheduling and bargaining lower than uncertainty, to designing bidding brokers in a large region of software parts, comparable to digital trade, offer chain administration, or key-phrase ads, to designing brokers which can effectively reflect real human behaviors in life like games.
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Extra 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
The time delay could correspond to the delay between persistent changes in the observations and the resulting effect in the mean agent behavior. e. the environment is not completely stochastic). The reason for valuing recent observation more is that customer demand in each market segment follows a Poisson distribution with a random walk. The lead time between a change in the market and its effect on other prices may be longer than one day, but the minimum possible lead time for this effect is 1 day.
The additive regression model estimates the difference between the next day price and the 20 day ahead price and is derived from a set of 31 visible features (16 product prices, 3 demand signals, storage cost, interest rate, and 10 unique component prices) from the current day’s observations. This technique is similar to the PLS regression method discussed in Section 3 but differs by not using information observed prior to the current day. Deep Maize estimates future product prices using the same k-nearest neighbor used in next day predictions but instead trained on 20 day ahead observations.
There are several aspects of PLS regression that could be explored to refine and expand on these results. First, in prediction mode, PLS regression can also measure prediction confidence on each observation by analysis of the residuals computed in the regression. Another direction of future research is related to analysis of the loading vectors of the PLS model to attempt to compute causal relationships between lagged variables. Additionally, this information could be used for manual validation of the computed models.