Download PDF by Luc Lamontagne, Mario Marchand: Advances in Artificial Intelligence: 19th Conference of the

By Luc Lamontagne, Mario Marchand

ISBN-10: 3540346287

ISBN-13: 9783540346289

This booklet constitutes the refereed complaints of the nineteenth convention of

the Canadian Society for  Computational reports of Intelligence, Canadian

AI 2006, held in Québec urban, Québec, Canada in June 2006.

The forty seven revised complete papers provided have been conscientiously reviewed and chosen

from 220 submissions. The papers are equipped in topical sections on

agents, bioinformatics, constraint delight and disbursed seek,

knowledge illustration and reasoning, common language, reinforcement

learning and, supervised and unsupervised learning.

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Extra resources for Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City,

Example text

3 Example This section gives an example of how this algorithm works. Let the system is composed of a set of four agents: A = {a1 , a2 , a3 , a4 }. After the first deliberation process, all the coalition values are computed and sent across. Their values are as the followings: v1 v2 v3 v4 = 8 v12 = 12 v13 = 13 v14 = 6 v23 v24 v34 = 13 v123 = 16 v124 = 10 v134 = 18 v234 = 20 = 15 = 21 v1234 = 22 = 23 = 16 = 19 A Pruning-Based Algorithm 21 After exchanging the coalitions generated among each other, each agent can select for each cardinality its best coalition.

The recognition space o is composed of all the plans that can be classified between the lattice infinimum and supremum, such as o = {WashDish, PrepareTea, WashDish ∇ PrepareTea, WashDish Δ PrepareTea, I1 , I2 }. These set constitute the whole plausible plans that can explain the behaviour of the occupant. Now, let us 34 B. Bouchard, S. Giroux, and A. Bouzouane suppose that a second observation GetW ater(o2 ) was detected. The new interpretation model would then be the sub-lattice upper bounded by P repareT ea and lower bounded by W ashDishΔP repareT ea, as shown in Figure 2.

This action structure is then classified according to a taxonomy of low-level actions to identify its conceptual type. Thereafter, the LAR agent notifies the cognitive assistant that a new low-level action is detected and it sends the actions type. Actually, we suppose that the low-level sensors give us correct inputs. Another DOMUS team is working on detection and isolation of sensors failure, in order to relax this strong assumption and to minimize low-level uncertainty. 1 High-Level Recognition Service The assistant agent is equipped with a high-level recognition service (HLRS), which provides an interpretation model of the occupant behaviour as input to assistance service.

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Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, by Luc Lamontagne, Mario Marchand


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