By Jiuyong Li

This e-book constitutes the refereed court cases of the twenty third Australasian Joint convention on man made Intelligence, AI 2010, held in Adelaide, Australia, in December 2010. The fifty two revised complete papers offered have been rigorously reviewed and chosen from 112 submissions. The papers are equipped in topical sections on wisdom illustration and reasoning; info mining and information discovery; laptop studying; statistical studying; evolutionary computation; particle swarm optimization; clever agent; seek and making plans; ordinary language processing; and AI functions.

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Additional resources for AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings

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Each of the activities occupies a single record in the journal. For the purpose of simplicity in the remainder of the paper, we will use the terms I-record, R-record, and W-record to respectively refer to these activities.

Definition 2. The initial sequents of SLL are of the form: for any propositional variable p, ˆ ⇒ [d]p ˆ [d]p ˆ ⇒ [d]1 ˆ Γ ⇒ [d] ˆ [d]⊥, Γ ⇒ γ. co) (;left) (;right). α, Γ ⇒γ [d][b Γ ⇒ [d][b Note that Girard’s intuitionistic linear logic ILL is a subsystem of SLL. The ˆ ⇒ [d]α ˆ for any formula α are provable in cut-free SLL. sequents of the form [d]α We now define a sequence-indexed phase semantics for SLL. The difference between such a semantics and the original phase semantics for ILL by Girard [1] is the definition of the valuations: whereas the original semantics has a valuation ˆ v, our semantics has an infinite number of sequence-indexed valuations v d (dˆ ∈ ∅ SE), where v just works as v.

In this work, we aimed to adopt his framework in its entirety. The last framework of interest is the generalized update that integrates the belief revision and update methods, in conjunction with possible events, to draw the best explanation for the new information [9]. The generalized update employs three Spohnian style, cardinal κ-functions for belief states, the possible events in relation to states, and, the possible outcomes of events. However, to apply the iterated generalized update, we require to employ the Markovian assumption that is akin to our future work [3].

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