By Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne, Mario Marchand (eds.)
This booklet constitutes the refereed complaints of the nineteenth convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2006, held in Qu?bec urban, Qu?bec, Canada in June 2006.
The ebook offers forty seven revised complete papers, conscientiously reviewed and chosen from 220 submissions. The papers are prepared in topical sections on brokers, bioinformatics, constraint pride and dispensed seek, wisdom illustration and reasoning, normal language, reinforcement studying and, supervised and unsupervised studying. contains CD-ROM.
Read Online or Download Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006. Proceedings PDF
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Extra info for Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006. Proceedings
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. Let us now suppose that the assistant agent has detected that the inhabitant remains still for a certain period of time. In such a case, the assistant agent will have to increase his autonomy level by taking control of the home using the intention schema of the inhabitant, deﬁned by the inﬁnimum Δinf = (x ◦ GoT oKitchen) of the lattice, to predict what the person wanted to do.
In order to prove this, we addressed several questions, such as which geographic entities should be modeled as agents? Which kinds of interactions should exist between agents? And how can agents improve the personalization, generation, and transfer of maps? 5 Agents’ Interactions A map expresses a geographic reality, according to a specific scale and purpose. This reality cannot be accurately represented when the scale of the map is reduced (Fig. 2). Indeed, scale reduction often diminishes the map’s legibility which then requires some modifications in order to be improved.
We test our algorithm down to the 5th best coalitions only. As our algorithm deals with non-superadditive environments, this setting tends to increase the cooperation cost of a coalition as its size grows. Hence it forces agents to work harder to form proﬁtable coalitions and to achieve optimal coalition structures. Both algorithms uses the Simplex algorithm to ﬁnd the optimal solution for each coalitions. Figure 1 compares the performance of our algorithm against that of exhaustive search. The left x-axis is the number of coalition structures generated while the right x-axis is the elapsed time spent for generating optimal coalition structures in milliseconds.
Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June 7-9, 2006. Proceedings by Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne, Mario Marchand (eds.)