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Download Autonomous Agents and Multiagent Systems: AAMAS 2016 by Nardine Osman, Carles Sierra PDF

By Nardine Osman, Carles Sierra

This booklet includes a number of top papers from eleven workshops held on the overseas convention on independent brokers and Multiagent platforms, in Singapore in may possibly 2016.The eleven complete papers have been rigorously reviewed and chosen for inclusion during this quantity. They conceal particular issues, either theoretical and utilized, within the normal zone of independent brokers and multiagent systems.

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Extra info for Autonomous Agents and Multiagent Systems: AAMAS 2016 Workshops, Best Papers, Singapore, Singapore, May 9-10, 2016, Revised Selected Papers

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5] prove that the mechanism has pure Nash equilibria while Ekici and Kesten [14] study the ordinal equilibria of the mechanism and prove that the desirable properties of the mechanism are not necessarily satisfied for those profiles. Another, somewhat different recent branch of study considers ordinal measures of efficiency instead of social welfare maximization, under the assumption that agents’ preferences are only expressed through preference orderings over items. Bhalgat et al. [8] study the approximation ratio of matching mechanisms, Social Welfare in One-Sided Matching Mechanisms 33 when the objective is maximization of ordinal social welfare, a notion of efficiency that they define based solely on ordinal information.

Over the years, several different mechanisms have been proposed with various desirable properties related to truthfulness, fairness and economic efficiency with Probabilistic Serial [1,7–9] and Random Priority [1,2,4,9,15,23] being the two prominent examples. As mentioned earlier, in settings without money, one needs to represent the valuations in some canonical way. e. each agent has a total value of 1 for all the items. Intuitively, this normalization means that each agent has equal influence within the mechanism and her values can be interpreted as “scrip money” that she uses to acquire items.

The probability of any 1 . Furthermore, suppose that when a team task being uncovered is m · m12 = m Evaluating the Efficiency of Robust Team Formation Algorithms 23 2 reaches (q − 1)-robustness, we run the algorithm for k−q ln n iterations. Then, the probability of a task being uncovered during this series of iterations is 1 . Run this algorithm for q = 0, 1, . . , k; the probability n(e−k+q )2/(k−q) ln m = m of having any tasks uncovered during any of these steps becomes k−1 q=0 m−1 = m m−1 m k .

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