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Extra info for Stairs 2008: Proceedings of the Fourth Starting AI Researchers’ Symposium
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Thielscher: Ramification and causality, Artificial Intelligence, (1997), 89(1-2), 317–364. [23] S. Valentini: Decidability in intuitionistic type theory is functionally decidable, Math. Logic, 42 (1996), 300–304. [24] S. Vassos and H. Levesque: Progression of Situation Calculus Action Theories with Incomplete Information, Procs. of the Int. Joint Conference on Artificial Intelligence (IJCAI’07, (2007), 2029–2034. 24 STAIRS 2008 A. Cesta and N. ) IOS Press, 2008 © 2008 The authors and IOS Press. All rights reserved.
Bonnet and C. 2. Computation of the coalition structure Each agent ai generates the current coalition structure as follows: 1. ai organizes the set of tasks Taτi as a partition {T1 . . Th } according to the compound tasks; Example 4 Let Taτi be {t1 , t2 , t3 , t4 , t5 }. Let us suppose that tasks t1 and t2 form a compound task as well as t4 and t5 . Then Taτi is organized as {{t1 , t2 }, {t3 }, {t4 , t5 }}. 2. each Ti is the goal of a single potential coalition; as subsets Ti are disjoint3 , the number of potential coalitions generated by agent ai is given by the number of compound tasks ai knows; 3.
Computing the similarity between two items from a user-item matrix. e. between rows of the rating matrix). Then, the active user is associated to the nearest community according to the correlation measure. Members of this community are the most appropriate users to consider since they have common interests with the active user. The closer users are to the active user, the more their preferences are taken into account: this is the pairwise prediction phase. At last, the prediction aggregation consists in computing the weighted mean of the community’s ratings in order to provide an estimated vote for each unrated item.