By Jiuyong Li

ISBN-10: 3642174310

ISBN-13: 9783642174315

This ebook constitutes the refereed court cases of the twenty third Australasian Joint convention on synthetic Intelligence, AI 2010, held in Adelaide, Australia, in December 2010. The fifty two revised complete papers awarded have been rigorously reviewed and chosen from 112 submissions. The papers are prepared in topical sections on wisdom illustration and reasoning; information mining and data discovery; desktop studying; statistical studying; evolutionary computation; particle swarm optimization; clever agent; seek and making plans; normal language processing; and AI purposes.

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

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Journal of Symbolic Logic, 510–530 (1985) 2. : On the difference between updating a knowledge base and revising it. In: Principles of Knowledge Representation and Reasoning, pp. 387–394 (1991) 3. : A qualitative markov assumption and its implications for belief change. In: UAI, pp. 263–273 (1996) 4. : Modeling belief in dynamic systems, part ii: Revisions and update. AI/0307071 (2003) 5. : Belief update revisited. In: IJCAI, pp. 2517–2522 (2007) 6. : Modeling belief in dynamic systems, part i: Foundations.

In usual way, possible worlds are defined as w : AP → {0, 1}, and, W as the complete set of possible worlds. We assume the set of natural numbers N as the time domain. A history h is a function that assigns to each time point t ∈ N a member of W, h : N → W. The complete set of histories is denoted by H. Every history gives a complete picture of a dynamic world that is analogue to the possible worlds that give a complete picture of a static world. g. the example given in caption of figure 1. In addition, to represent beliefs at certain instants or intervals we define: 1.

The symptoms being newly discovered are the new information. The belief change approach that allows for such a specific representation of the new information is by R. C. Jeffrey where the new information is expressed as a probability distribution over a number of propositions [8] (Chapter 11). This representation also allows for the new information to be revisable. Although, Jeffery’s approach to belief change is probabilistic, his method is adopted in Spohn’s theory of ranking functions which is a deterministic/qualitative theory of belief change [7].

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AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings by Jiuyong Li

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