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Download e-book for iPad: AI 2008: Advances in Artificial Intelligence: 21st by Wayne Wobcke, Mengjie Zhang

By Wayne Wobcke, Mengjie Zhang

ISBN-10: 3540893776

ISBN-13: 9783540893776

This booklet constitutes the refereed lawsuits of the 21th Australasian Joint convention on man made Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008.

The forty two revised complete papers and 21 revised brief papers offered including 1 invited lecture have been rigorously reviewed and chosen from 143 submissions. The papers are prepared in topical sections on wisdom illustration, constraints, making plans, grammar and language processing, statistical studying, desktop studying, information mining, wisdom discovery, delicate computing, imaginative and prescient and photograph processing, and AI applications.

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Read Online or Download AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008, PDF

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Extra resources for AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008,

Example text

2. Different deals are in favour of different parties different parties. The major concern of a bargaining theory is how to measure and balance the gain of each negotiating party. Instead of counting the number of demands a deal contains for each party, we consider the top block demands a player keeps in the deal (the top levels of demands in each player’s demand hierarchy) and ignore all demands that are not included in the top blocks except the common demands3 . , max{k : Xi≤k ⊆ Di }. For instance, in Figure 2, player 1 can successfully remain maximally top k + 1 levels of his demands from deal D while player 2 gains maximally top k levels of his demands from the deal.

Hans van Ditmarsch acknowledges support of the Netherlands Institute of Advanced Study where he was Lorentz Fellow in 2008. References 1. : Forget it! In: AAAI Fall Symposium on Relevance, New Orleans (1994) 2. : Propositional independence: Formula-variable independence and forgetting. J. Artif. Intell. Res. (JAIR) 18, 391–443 (2003) 3. : Knowledge updates: semantics and complexity issues. Artificial Intelligence 164(1-2), 209–243 (2005) 4. : Knowledge forgetting: Properties and applications. Work in progress under submission (2008) 5.

Furthermore, we yield X1 ⊗1 F2 (G) = (X1 ⊗1 F2 (G))+ (Φ1 + Φ2 )+ (X1 ∩X2 ) because X1 ∩ X2 ⊆ F2 (G). Since F2 (G) ⊆ Φ1 + Φ2 + (X1 ∩ X2 ). According to the AGM postulates, we have (X1 ⊗1 F2 (G)) + (Φ1 + Φ2 + (X1 ∩ X2 )) = X1 ⊗1 (Φ1 +Φ2 +(X1 ∩X2 )). Therefore X1 ⊗2 F2 (G) = X1 ⊗1 (Φ1 +Φ2 +(X1 ∩X2 )). In addition, it is easy to prove that Φ1 ⊆ X1 ⊗1 (Φ2 + (X1 ∩ X2 )). By the AGM postulates again, we have X1 ⊗1 (Φ2 + (X1 ∩ X2 )) = (X1 ⊗1 (Φ2 + (X1 ∩ X2 )) + Φ1 = X1 ⊗1 (Φ1 + Φ2 + (X1 ∩ X2 )). Therefore X1 ⊗1 F2 (G) = X1 ⊗1 (Φ2 + (X1 ∩ X2 )).

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AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008, by Wayne Wobcke, Mengjie Zhang


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