Download Agent Computing and Multi-Agent Systems: 10th Pacific Rim by Ka-man Lam, Ho-Fung Leung (auth.), Aditya Ghose, Guido PDF

By Ka-man Lam, Ho-Fung Leung (auth.), Aditya Ghose, Guido Governatori, Ramakoti Sadananda (eds.)

This e-book constitutes the completely refereed post-workshop court cases of the tenth Pacific Rim foreign Workshop on Multi-Agents, PRIMA 2007, held in Bankok, Thailand, in November 2007.

The 22 revised complete papers and sixteen revised brief papers offered including eleven software papers have been rigorously reviewed and chosen from 102 submissions. starting from theoretical and methodological concerns to varied purposes in several fields, the papers tackle many present matters in multi-agent examine and development,

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Read Online or Download Agent Computing and Multi-Agent Systems: 10th Pacific Rim International Conference on Multi-Agents, PRIMA 2007, Bangkok, Thailand, November 21-23, 2007. Revised Papers PDF

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Additional info for Agent Computing and Multi-Agent Systems: 10th Pacific Rim International Conference on Multi-Agents, PRIMA 2007, Bangkok, Thailand, November 21-23, 2007. Revised Papers

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552–559 (2003) 7. : The complexity of decentralized control of markov decision processes. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI 2000), pp. 32–37 (2000) 8. : Communication for improving policy computation in distributed pomdps. In: Proceedings of the Third International Joint Conference on Agents and Multiagent Systems (AAMAS 2004), pp. 1098– 1105 (2004) 9. : Reexamination of the perfectness concept for equilibrium points in extensive games. International Journal of Game Theory 4, 25–55 (1975) 10.

In a Nash equilibrium, no agent has an incentive to unilaterally change strategy, assuming that no other agent changes its policies. A TPE 1 is proposed as a refinement of the concept of a Nash equilibrium. In a TPE, we assume each agent’s policy is a best response to other agents’ policies, even if other agents might deviate from the given policies with small probability . Definition 1 (Perturbed policy). A perturbed policy πit for a deterministic policy πi is a stochastic policy defined as follows: if πi chooses action ak in a certain situation, then, for the same situation, πit chooses ak with probability 1 − , and chooses each action aj (where j = k) with probability /(|Ai | − 1).

Expected Reward obtained by the JESP-NE and the JESP-TPE Fig. 3. Expected reward for listen cost c with tiger cost d = 14 initial state chosen randomly, and the initial/default policy is selected as Listen for all states. 0E − 13 so that the expected reward is not affected. Fig. 2 shows the expected reward of the two JESPs for three different settings where the tiger cost is 20. 25. Next, we increase the listen cost to 14 (setting 2). 70. Then we increase the finite horizon to 5, keeping the listen cost of 14 (setting 3).

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