By Byeong Ho Kang, Quan Bai
This publication constitutes the refereed lawsuits of the twenty ninth Australasian Joint convention on man made 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 purposes and concepts; giant info; constraint delight, seek and optimisation; wisdom illustration and reasoning; computing device studying and information mining; social intelligence; and textual content mining and NLP.
The complaints additionally comprises 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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Additional resources for AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings
The agent that does not use the resource gets a ﬁxed payoﬀ. All the agents using the resource get the same payoﬀ. Consequently, the more agents decided to use the resource, the smaller the obtainable payoﬀ per agent; and when the number of agents sharing the resource is higher than a certain threshold, it is better for the others not to use the resource. A simple utility function reﬂecting this game can be expressed as follows: U= 1 if agent decision is “no”, 101 − η if agent decision is “yes”.
Nguyen et al. a very large number of agents . 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.
Lemma 8. If a state w satisﬁes αn and n > 0 then w is a non-terminal state. Proof. This follows directly by applying Lemma 2 to Eqs. 1 and 2. Theorem 1. If a state w satisﬁes αn for some n then there exists a strategy for A that guarantees that a state satisfying α0 can be reached in at most n steps whenever the game is in the state w. Proof. We know from Lemmas 6 and 7 that if w satisﬁes αn and A plays optimally, then the next state will satisfy some αm with m < n. This means that no matter what strategy is played by B, in every round that follows some αm will be satisﬁed, and m will be decreasing with every new round.