By Martin Chapman, Gareth Tyson, Katie Atkinson (auth.), Esther David, Christopher Kiekintveld, Valentin Robu, Onn Shehory, Sebastian Stein (eds.)
This quantity comprises eleven completely refereed and revised papers detailing fresh advances in study on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially provided on the Joint Workshop on buying and selling Agent layout and research (TADA 2012) and Agent-Mediated digital trade (AMEC 2012) co-located with AAMAS 2012 in Valencia, Spain, in June 2012.
The expanding reliance on software program brokers has created a number of urgent new learn demanding situations, together with the layout of acceptable agent choice algorithms, ways for predicting the complicated behaviors and interactions of a number of brokers, together with the computation of equilibria, and the engineering of protocols and mechanisms that make sure digital markets behave in a strong demeanour or satisfy different fascinating standards. Drawing upon a various diversity of medical disciplines, together with machine technological know-how, economics, synthetic intelligence, operations examine and online game conception, the papers accumulated during this quantity signify a cross-section of contemporary learn and canopy subject matters similar to thoughts for person buying and selling brokers, the layout of markets and interplay protocols among brokers, and numerous applications.
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Additional resources for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC and TADA 2012, Valencia, Spain, June 4th, 2012, Revised Selected Papers
In trading agent design and analysis, JACK can facilitate the simulation of autonomous agents in a speciﬁc auction domain by providing auction simulation infrastructure and not restricting the developer’s language choice. 2. In experimental economics, JACK can facilitate user studies (either in the lab or online via Mechanical Turk ) by allowing researchers to test human behavior in a variety of diﬀerent auction domains, including environments with some autonomous and some human agents. 3. In artiﬁcial intelligence, JACK can be used to develop mixed-initiative bidding agents, in which human bidders receive recommended bids from an underlying autonomous bidding agent and are free to accept or reject those recommendations as they see ﬁt.
Max(daily cycles − 2000, 0) · lost cycle cost. (16) Policy Search through Adaptive Function Approximation 25 Table 1. The weights of the logistic regression ﬁt. The most important features are the oﬀer price (OP) and the average maximum report price (AMAX), having negative and positive eﬀect respectively on the probability of a successful bid the higher they are. Third in importance is the reserve price (RP). Importance is based on their contribution to the ﬁnal outcome and is comparable due to normalization performed before ﬁtting.
Mertacor also behaves in a predictive manner. A risk factor R determines the agent’s eagerness to take risks in order to pursue greater proﬁt. R values range in the interval [−c, c], where c deﬁnes the maximum deviation of the ﬁnal shout price from the successful price. The success rate of the agent’s M latest shouts is computed as follows: k= M i=1 acceptedShouts(i) M (5) where acceptedShouts(i) returns 1 or 0 if the shout was successful or not respectively. Equation (5) is then normalized to the interval [−c, c]: kˆ = c · (2k − 1) (6) ˆ For example, if the Note that the risk factor could be assigned the value of k.
Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC and TADA 2012, Valencia, Spain, June 4th, 2012, Revised Selected Papers by Martin Chapman, Gareth Tyson, Katie Atkinson (auth.), Esther David, Christopher Kiekintveld, Valentin Robu, Onn Shehory, Sebastian Stein (eds.)