By Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen Cranefield, Abhaya Nayak (eds.)
This publication constitutes the refereed lawsuits 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 awarded have been rigorously reviewed and chosen from one hundred twenty submissions. The papers are geared up in topical sections as brokers; AI functions; cognitive modelling; laptop imaginative and prescient; constraint pride, seek and optimisation; evolutionary computation; video game enjoying; wisdom illustration and reasoning; laptop studying and information mining; usual language processing and knowledge retrieval; making plans and scheduling.
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 info for AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings
Princeton University Press, Princeton (1976) 10. : Classic works of the Dempster-Shafer theory of belief functions: An introduction. , Liu, L. ) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol. 219, pp. 1–34. Springer, Heidelberg (2008) 11. : Decision analysis using belief functions. International Journal of Approximate Reasoning 4(5), 391–417 (1990) 12. : A model for decision making with missing, imprecise, and uncertain evaluations of multiple criteria.
387–403. Springer, Heidelberg (2013) 13. : Merging information under constraints: a logical framework. Journal of Logic and Computation 12(5), 773–808 (2002) 14. : Inﬁnitary belief revision. Journal of Philosophical Logic 30(6), 525–570 (2001) 15. : Merging databases under constraints. International Journal of Cooperative Information Systems 7(01), 55–76 (1998) 16. : 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.
Given the set of all possible allocations A, agent i has a valuation vi (a, ti ) for the allocation a ∈ A when its type is ti . Assume that the status quo allocation a ˜ ∈ A deﬁnes the agents’ reservation utilities. We will normalise each valuation a, ti ) = 0. Assume also that the maximum amount of function vi such that vi (˜ resource available for this allocation is R. Thus, an allocation (a1 , . . , an ) is n feasible iﬀ i=1 ai ≤ R. If the status quo allocation a ˜ = (˜ a1 , . . , a ˜n ) is feasible then gains from trade ˜ = R− n a ˜ .
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.)