By Sebastian Haufe, Michael Thielscher (auth.), Jiuyong Li (eds.)
This publication constitutes the refereed complaints 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 provided have been rigorously reviewed and chosen from 112 submissions. The papers are equipped in topical sections on wisdom illustration and reasoning; information mining and data discovery; laptop studying; statistical studying; evolutionary computation; particle swarm optimization; clever agent; seek and making plans; common language processing; and AI applications.
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Additional info for AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings
Then, following the Spohn’s method by which he shows his usual conditionalization function is analogue to the AGM revision operation, we can show that the temporal conditionalization presented here is an AGM compatible belief change operation [7,11]. We do not show this claim formally due to (1) the space constraint and (2) that the traditional temporal logics are designed to express the general properties of a system where the specific instants are not relevant. However, in our theory of belief change, each piece of information corresponds to specific time points.
Proof. The provability of SLL− can be transformed into that of ILL− by Theorem 18. Since ILL− is decidable, SLL− is also decidable. Moreover, f in Deﬁnition 17 is a polynomial-time reduction. Since ILL− is PSPACE-complete , SLL− is also PSPACE-complete. D. 4 Applications We now consider an example of specifying secure password authentications on a network using SLL formulas. Consider a network that consists of three clients and a server. In order to obtain access to the server system, each client must try to login to the system in less than three attempts.
It must not make any presumption about the current state of the frames structure. 1 In fact with this demand, we want the mechanism to be open to further extensions. More specifically the causality tracking mechanism is intended to override the interpretation mechanism's behaviour in cases of recovering from an error. 1 Journaling System With having basic nested frame structure,2 we extended each frame with a journal to track its activity during a dialogue. The activity involves frame slot interpretation, information reading, and information writing.
AI 2010: Advances in Artificial Intelligence: 23rd Australasian Joint Conference, Adelaide, Australia, December 7-10, 2010. Proceedings by Sebastian Haufe, Michael Thielscher (auth.), Jiuyong Li (eds.)