Download PDF by Byeong Ho Kang, Quan Bai: AI 2016: Advances in Artificial Intelligence: 29th
By Byeong Ho Kang, Quan Bai
This publication 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 provided 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 concepts; substantial info; constraint delight, seek and optimisation; wisdom illustration and reasoning; desktop studying and information mining; social intelligence; and textual content mining and NLP.
The court cases additionally includes 2 contributions of the AI 2016 doctoral consortium and six contributions of the SMA 2016.
Read 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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Extra info for AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings
Evolution of system fairness index by diﬀerent algorithms. D. Nguyen et al. 5 150 Exhaustive search Proposed algorithm Regret-based RL CODIPAS-RL 200 250 300 350 400 Number of agents Fig. 3. 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.
Evidently, D1 is a closed, convex subspace of Rm×m . Deﬁne the Lyapunov function P (w) = 12 w 2 , with ∇P (w) = w. Then P satisﬁes the following properties and thus is a potential function for D1 : • P is continuously diﬀerentiable; • P (w) = 0 ⇔ w ∈ D1 ; • ∇P (w), w > 0 for all w ∈ / D1 . Let ϕ : Rm×m → 2X given by ϕ(w) = (1 − δn ) μ(w) + X, δn ,w∈ / D1 m w ∈ D1 (8) where μ(w) denotes a probability vector computed from the matrix w = Bn according to the process above. Deﬁne a correspondence N on Rm×m \ D1 by N (w) = C(ϕ(w) × Y ) so that ϕ is N -adapted, which means N (w) contains all resulting average regrets.
A player loses the strategic argumentation game when she is unable to achieve her desired outcome. In general, both players can win if the argumentation reaches a state that is desired by both players, but in this paper we consider an adversarial setting where the players’ aims are mutually exclusive. Strategic abstract argumentation is formalized as follows . We assume there are two players, a proponent P and her opponent O. A split argumentation framework (ACom , AP , AO , ) consists of three sets of arguments: ACom the arguments that are common knowledge to P and O; AP the arguments availover able to P , and AO the arguments available to O; and an attack relation ACom ∪ AP ∪ AO .
AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings by Byeong Ho Kang, Quan Bai