AI 2016: Advances in Artificial Intelligence: 29th by Byeong Ho Kang, Quan Bai

By Byeong Ho Kang, Quan Bai

This ebook constitutes the refereed lawsuits 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 conscientiously reviewed and chosen from 121 submissions. The papers are prepared in topical sections on brokers and multiagent platforms; AI purposes and strategies; vast information; constraint pride, seek and optimisation; wisdom illustration and reasoning; laptop studying and information mining; social intelligence; and textual content mining and NLP.

The court cases additionally comprises 2 contributions of the AI 2016 doctoral consortium and six contributions of the SMA 2016.

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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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In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), pp. 1671–1677 (2015) Corrupt Strategic Argumentation: The Ideal and the Naive Michael J. au Abstract. Previous work introduced a model of corruption within strategic argumentation, and showed that some forms of strategic argumentation are resistant to two forms of corruption: collusion and espionage. Such a result provides a (limited) basis on which to trust agents acting on our behalf. That work addressed several argumentation semantics, all built on the notion of admissibility.

Comparison of fairness between algorithms for the same number of iterations. Regret-based RL (up to 900 iterations), especially the later. In fairness metric, our algorithm also leads to the highest system fairness index under the same number of iterations, as compared to the other RL schemes. The Regret-based RL scheme performs poorest due to its slow convergence speed. To further study the impact of the total number of agents in the game on algorithms performance, we vary the agent number from 150 to 400 and measure the performances of all algorithms in fairness metric.

After all, B may choose a different action in order to prevent this. Therefore, for states in which B is the active player we demand that all actions of B lead to a state satisfying αn−1 . Let us first define a formula αn as follows: αn = C ∞ (N (αn−1 ∨ αn−2 ∨ . . α0 )). Then, for n is even, we can define: α αn = C ∞ ¬terminal ∧ ¬ ∃(legal(t) ∧ ¬Xt n ) (2) t∈S where S is the same set of action-terms as the one that appears in the ANF of α αn and the Xt n are also obtained from the ANF of αn . Lemma 7.

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