By Nardine Osman, Carles Sierra

ISBN-10: 3319468812

ISBN-13: 9783319468815

ISBN-10: 3319468820

ISBN-13: 9783319468822

This e-book incorporates a number of most sensible papers from eleven workshops held on the foreign convention on self reliant brokers and Multiagent structures, in Singapore in may well 2016.The eleven complete papers have been rigorously reviewed and chosen for inclusion during this quantity. They disguise particular subject matters, either theoretical and utilized, within the normal sector of self sustaining brokers and multiagent systems.

**Read Online or Download Autonomous Agents and Multiagent Systems: AAMAS 2016 Workshops, Best Papers, Singapore, Singapore, May 9-10, 2016, Revised Selected Papers PDF**

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

**Example text**

We observed that its relative performance was not aﬀected by the number of tasks. Since the size of the search space for the GA increases exponentially only with the number of agents in the problem increasing, this is not a surprising trend. Moreover, since GAs search locally about existing solutions to make improvements, NSGA-II often ﬁnds near-optimal results, as shown in Table 1. Since the NSGA-II algorithm computes the entire Pareto frontier of solutions, it runs much slower than the greedy approximation and the linear program.

Then a strategy profile q is called a 1. coarse correlated equilibrium if E [ui (Mi (s))] ≥ E [ui (Mi ((si , s−i )))], s∼q s∼q 2. Bayes-Nash equilibrium for a distribution Δu where each (Δu )i is independent, if when u ∼ Δu then q(u) = ×i qi (ui ) and for all ui in the support of (Δu )i , E [ui (Mi (s))] ≥ u−i ,s∼q(u) E [ui (Mi (si , s−i ))] u−i ,s−i ∼q−i (u−i ) where the given inequalities hold for all agents i, and (pure) deviating strategies si . Also notice that for randomized mechanisms definitions are with respect to an expectation over the random choices of the mechanism.

Next, notice that since agent i starts consuming item j at time 0 in s∗ and all other agents use the same strategies in s and s∗ , it holds that every agent k = i starts consuming item j in s∗ no sooner than she does in s. This means that in proﬁle s∗ , agents other than i will need more time to consume β · α; in particular they will need at least βtj (s) time, so tj (s∗ ) ≥ βtj (s). However, from the previous paragraph we know that they will consume at least 12 − 14 tj (s), so letting β = α1 12 − 14 tj (s) we get 1 1 1 − · tj (s) 2 4 α 1 1 1 − · tj (s) ≥ · tj (s) 2 4 4 tj (s∗ ) ≥ βtj (s) ≥ tj (s) ≥ tj (s) Now we can lower bound the utility of an agent at any pure Nash equilibrium.

### Autonomous Agents and Multiagent Systems: AAMAS 2016 Workshops, Best Papers, Singapore, Singapore, May 9-10, 2016, Revised Selected Papers by Nardine Osman, Carles Sierra

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