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 offered 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 provided at those workshops illustrate either the intensity and wide variety of analysis issues during this box. they vary from supplying recommendations to open theoretical difficulties in on-line scheduling and bargaining lower than uncertainty, to designing bidding brokers in a large quarter of program components, resembling digital trade, offer chain administration, or key-phrase ads, to designing brokers that may effectively mirror genuine human behaviors in life like 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
Finally, information about all other products is expected to contain the least information density (Class P4h). By constraining 3 This symbol refers to the lack of any observation. Improving Prediction in TAC SCM Class: C2 Demand in Market Segs. 1, 2, 3 37 Class: C4 Component comp 401, supplier 7 Class: C4b All Lines Producing this Component: suppliers 7, 8 Class: C3 All Products Using This Component: SKU 1, 3, . . Class: C4h All Components: 100, . . , 401 Class: C3c All Products: SKU 1, . .
Table 2. 3. Mertacor-RL has the ability to adapt α, but is not equipped with k-NN. Last but not least, Mertacor-Finals combines all the modifications and extensions described in this paper and is the agent participated in the 2010 finals. In the first tournament, two agents from each one of the four versions of the agent competed, having all the same storage capacity equal to C MED . In the second tournament the storage capacities were selected in competition terms (2 agents with C LOW , 4 with C MED and 2 with C HIGH ).
Moreover, we believe that by optimizing these techniques, now implemented rather crudely, more profit is possible. It is also evident that including both extensions has more benefits than anyone of them alone. When dealing with different capacities between games, the domain becomes more challenging, especially for on-line learning methods. In Table 4, one can find the results of the second tournament. Again the differences are not large enough, but there are small deviations between versions. The k-NN version is able to estimate better the capacity Table 3.