Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen's AI 2013: Advances in Artificial Intelligence: 26th PDF

By Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen Cranefield, Abhaya Nayak (eds.)

ISBN-10: 3319036793

ISBN-13: 9783319036793

ISBN-10: 3319036807

ISBN-13: 9783319036809

This publication constitutes the refereed court cases of the twenty sixth Australasian Joint convention on synthetic Intelligence, AI 2013, held in Dunedin, New Zealand, in December 2013. The 35 revised complete papers and 19 revised brief papers provided have been rigorously reviewed and chosen from a hundred and twenty submissions. The papers are prepared in topical sections as brokers; AI functions; cognitive modelling; computing device imaginative and prescient; constraint delight, seek and optimisation; evolutionary computation; video game enjoying; wisdom illustration and reasoning; desktop studying and knowledge mining; usual language processing and data retrieval; making plans and scheduling.

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Read Online or Download AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings PDF

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Extra resources for AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings

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However, security resources that can be allocated are usually limited. And the attacker can observe the defender, for a period of time, to understand his security strategy and then attack a target accordingly. So, the defender has to take a random strategy to provide a security cover for everything. That is, the defender should find an optimal mixed strategy to maximise his expected utility. , [1, 3, 5]) on the topic of security games is to find the strong Stackelberg equilibrium [4]. That is, the attacker has perfect knowledge of the defender’s optimal strategy and accordingly chooses an optimal response strategy to maximise his expected utility.

75 consisting of 3418 proteins belonging to the 27 folds that was originally used in the DD with less than 40% sequential similarities. We also use the TG benchmark [18] consisting of 1612 proteins belonging to 30 folds with less than 25% sequential similarities. 3 Physicochemical-Based Attributes In this study, we investigate the impact of our proposed approaches using 15 physicochemical-based attributes. These 15 attributes have been selected from 117 physicochemical-based attributes (which are taken from the AAindex [20], the APDbase [21], and previous studies found in the literature [22]) in the following manner.

Strat [11] defines the expected utility interval as follows: Definition 3. Given a choice of c corresponding to mass function m over the possible consequence set, denoted as Θ, of all the choices, let u(ai , c) be the utility of choice c corresponding to element ai in the focal element A. Then the expected utility interval of choice c is EU I(c) = [E(c), E(c)], where E(c) = A⊆Θ min{u(ai , c) | ai ∈ A}m(A), (2) E(c) = A⊆Θ max{u(ai , c) | ai ∈ A}m(A). (3) Here E(c) and E(c) are called the lower and high boundaries of choice c, respectively.

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AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings by Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen Cranefield, Abhaya Nayak (eds.)


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