By Luc Lamontagne, Mario Marchand
This booklet constitutes the refereed court cases of the nineteenth convention of
the Canadian Society for Computational experiences of Intelligence, Canadian
AI 2006, held in Québec urban, Québec, Canada in June 2006.
The forty seven revised complete papers offered have been conscientiously reviewed and chosen
from 220 submissions. The papers are prepared in topical sections on
agents, bioinformatics, constraint pride and dispensed seek,
knowledge illustration and reasoning, traditional language, reinforcement
learning and, supervised and unsupervised learning.
Read or Download Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, PDF
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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,
To be able to plan a future assistance task, the agent must initially understand Peter’s intentions by building a minimal interpretation model describing the plausible plans that can explain his behaviour at this speciﬁc moment. This model takes the form of a lattice built following our recognition model, as shown in Figure 2. On the left of the ﬁgure, one may see the description of low-level actions (top left) and the high-level activities (bottom left) that has been recognized by the system. On the top right, one can see a graphical tool built in SVG3 (here showing the kitchen) that allows us to simulate the activation of the various environment sensors by clicking on the corresponding graphical objects.
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.
They consider deliberation cost in terms of monetary cost. The agents’ payoﬀs A Pruning-Based Algorithm 10 50 23 10 10 10 8 10 Elapsed Time for Generating Partitions by Our Algorithm No of Coalition Structures by Exhaustive Search 10 10 10 30 6 10 No of Coalition Structures by Our Algorithm 4 10 20 10 10 10 0 0 10 30 20 Number of Agents 40 2 Elapsed Time For Generating Optimal Coalition Structures Number of Coalition Structures Elapsed Time for Generating Partitions by Exhaustive Search 40 0 10 50 Fig.
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