By Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen Cranefield, Abhaya Nayak (eds.)
This e-book constitutes the refereed complaints of the twenty sixth Australasian Joint convention on man made Intelligence, AI 2013, held in Dunedin, New Zealand, in December 2013. The 35 revised complete papers and 19 revised brief papers offered have been conscientiously reviewed and chosen from one hundred twenty submissions. The papers are prepared in topical sections as brokers; AI functions; cognitive modelling; desktop imaginative and prescient; constraint delight, seek and optimisation; evolutionary computation; video game taking part in; wisdom illustration and reasoning; desktop studying and knowledge mining; typical language processing and knowledge retrieval; making plans and scheduling.
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Extra info for AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings
5 = Conclusion and Related Work This paper proposes a logical framework for bargaining with integrity constraints. More speciﬁcally, we construct a simultaneous concession solution to 12 X. Jing, D. Zhang, and X. Luo the bargaining game, which satisﬁes ﬁve logical and two game theoretical properties. Moreover, we prove that our solution can be characterised uniquely by the ﬁve logical properties. This work is built up on Zhang’s framework on the logical axiomatic model of bargaining in  but extends his work in several aspects.
On the logic of merging. In: KR 1998: Principles of Knowledge Representation and Reasoning, pp. 488–498. cn Abstract. There has been signiﬁcant recent interest in security games, which are used to solve the problems of limited security resource allocation. In particular, the research focus is on the Bayesian Stackelberg game model with incomplete information about players’ types. , the defender could not precisely have the probability of each type of the attacker. To address this issue, we deﬁne a new kind of security games with ambiguous information about the attacker’s types.
The cases of two, three, four, and ﬁve strategies (targets) are shown in Figure 1 (a), (b), (c), and (d), respectively. , δ(md ) > 0). However, in the URP based solution algorithm of SGAAT, the probability is still md (B) . Then, when there are three or more types, the games with the precise by |T a |−1 URP based solution algorithm of SGAAT are still Bayesian games, but the games with the D-S theory based solution algorithm of SGAAT are not. 1 For example, by formula (19), when 1 In the D-S theory based solution algorithm of SGAAT, the types have imprecise probability except the ﬁrst type.