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Byeong Ho Kang, Quan Bai's AI 2016: Advances in Artificial Intelligence: 29th PDF

By Byeong Ho Kang, Quan Bai

ISBN-10: 3319501267

ISBN-13: 9783319501260

ISBN-10: 3319501275

ISBN-13: 9783319501277

This booklet constitutes the refereed complaints of the twenty ninth Australasian Joint convention on synthetic Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016.

The forty complete papers and 18 brief papers awarded including eight invited brief papers have been rigorously reviewed and chosen from 121 submissions. The papers are prepared in topical sections on brokers and multiagent structures; AI functions and concepts; great facts; constraint delight, seek and optimisation; wisdom illustration and reasoning; computer studying and information mining; social intelligence; and textual content mining and NLP.

The complaints additionally includes 2 contributions of the AI 2016 doctoral consortium and six contributions of the SMA 2016.

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Read Online or Download AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings PDF

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Additional resources for AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings

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29–41, 2016. D. Nguyen et al. a very large number of agents [2]. Another challenge of RL-based algorithms is the inefficient of exploration. Since agents running RL procedure do not have a global knowledge of the whole system, they often require a high exploration times in order to converge to a stable equilibrium. In many application, these behaviours can result in undesirable outcomes [4,7]. This paper develops a new RL procedure that follows the regret-based principles [3,8] to overcome the disadvantage of slow speed and inefficient convergence of standard RL solutions.

Theorem 2. If all agents follow the proposed procedure, the empirical distribution of joint play of all agents zn (s) converges almost surely as t → ∞ to the set of correlated equilibria in the action space, for finite payoffs. Proof. The proof follows from how the “regret” measure is defined. Recall that [C(zn )]j,k = ∈L = zn (j, sn ∈S:in =j n ) (U (k, n ) − U (j, zn (sn ) (U (k, n) n )) − U (sn )) , where sn = (in , n ) is the joint play made at stage n. On any convergent subsequence lim zn → Π, we get n→∞ lim [C(zn )]j,k = n→∞ sn ∈S:in =j Π(sn ) (U (k, n) − U (sn )) ≤ 0.

Since player one cannot compute the first term as it only has access to the payoffs corresponding to actions it actually took, following [3], define an estimate of this term by ˜ (k, y) 1{i=j} = p(j) U (k, y) 1{i=k} . U p(k) which is computed from the regrets associated with the alternative action k weighted proportional to the relative probabilities of player one choosing action j versus k when those actions were actually taken. The associated pseudo regret matrix at stage n is now pn (j) C˜n (j, k) = U (k, yn ) 1{in =k} − U (j, yn ) 1{in =j} .

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AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings by Byeong Ho Kang, Quan Bai


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