Download Adaptive Agents and Multi-Agent Systems III. Adaptation and by Ana L. C. Bazzan, Denise de Oliveira (auth.), Karl Tuyls, PDF

By Ana L. C. Bazzan, Denise de Oliveira (auth.), Karl Tuyls, Ann Nowe, Zahia Guessoum, Daniel Kudenko (eds.)

This ebook comprises chosen and revised papers of the ecu Symposium on Adaptive and studying brokers and Multi-Agent structures (ALAMAS), versions 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The aim of the ALAMAS symposia, and this linked e-book, is to extend wisdom and curiosity in variation and studying for unmarried brokers and mul- agent platforms, and inspire collaboration among computer studying specialists, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a consultant overviewof present country of a?airs during this region. it really is an inclusive discussion board the place researchers can current contemporary paintings and speak about their latest principles for a ?rst time with their friends. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent structures, with a selected emphasis on how one can regulate proven studying ideas and/or create new studying paradigms to deal with the various demanding situations provided through advanced real-world difficulties. those symposia have been a very good good fortune and supplied a discussion board for the pres- tation of latest rules and effects referring to the notion of edition and studying for unmarried brokers and multi-agent structures. Over those 3 variants we acquired fifty one submissions, of which 17 have been rigorously chosen, together with one invited paper of this year’s invited speaker Simon Parsons. it is a very c- petitive reputation expense of roughly 31%, which, including evaluation cycles, has ended in an effective LNAI quantity. we are hoping that our readers might be encouraged via the papers integrated during this volume.

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Extra resources for Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning: 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems, Revised Selected Papers

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SARA 2005. LNCS (LNAI), vol. 3607, pp. 194–205. edu Abstract. In agent-based computational economics, many different trading strategies have been proposed. Given the kinds of market that such trading strategies are employed in, it is clear that the performance of the strategies depends heavily on the behavior of other traders. However, most trading strategies are studied in homogeneous populations, and those tests that have been carried out on heterogeneous populations are limited to a small number of strategies.

Details can be found in [4,5]. The optimal expected cost-to-go J, by definition the expected cost-to-go (2) minimized over all controls, satisfies the stochastic Hamilton-Jacobi-Bellman (HJB) equation n −∂t J = min u a=1 1 1 ua Rua + (ba + Bua ) ∂xa J + Tr σσ ∂x2a J 2 2 + V, with boundary condition J(x, T ) = φ(x). The minimization with respect to u yields equation (3), which specifies the optimal control for each agent. Substituting these controls in the HJB equation gives a non-linear equation for J.

For any pair of functions Q and Q , it is true that T (Q) − T (Q ) ∞ ≤ γ Q − Q ∞ . The Q-value iteration (Q-iteration, for short) algorithm starts from an arbitrary Q-function Q0 and in each iteration κ updates the Q-function using the formula Qκ+1 = T (Qκ ). From Theorem 1, it follows that T has a unique fixed point, and since from (4) this point is Q∗ , the iterative scheme converges to Q∗ as κ → ∞. Q-iteration uses an a priori model of the task, in the form of the transition and reward functions f , ρ.

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