Download Advances in Artificial Intelligence: 19th Conference of the by Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne, PDF

By Michael Y. K. Cheng, Robin Cohen (auth.), Luc Lamontagne, Mario Marchand (eds.)

ISBN-10: 3540346287

ISBN-13: 9783540346289

This publication constitutes the refereed lawsuits 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 e-book offers forty seven revised complete papers, rigorously reviewed and chosen from 220 submissions. The papers are geared up in topical sections on brokers, bioinformatics, constraint pride and disbursed seek, wisdom illustration and reasoning, ordinary language, reinforcement studying and, supervised and unsupervised studying. comprises CD-ROM.

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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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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

This publication 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 provides forty seven revised complete papers, conscientiously reviewed and chosen from 220 submissions.

Additional 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

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Where σ(α) = (an , . , σ(x), . , σ(y), . , a1 ) corresponds to an instantiation of α, computed by substituting each action 28 B. Bouchard, S. Giroux, and A. Bouzouane variable in α by the corresponding action sequence specified in σ. The only possible substitution for an action a is itself such that: ∀a ∈ A then σ(a) = a. Definition 3. α(an ◦ · · · ◦ x ◦ · · · ◦ a1 ) is a variable plan if and only if there exists a substitution σ(x) ∈ 2A such that α(an ◦ · · · ◦ σ(x) ◦ · · · ◦ a1 ) is a consistent plan.

Therefore, the model that we designed formally structures the recognition process to take this reality into account. In order to algebraically define our recognition model, we need first to show an overview of the action model on which it is based. This action model is described in great detail in [5]. 1 Action Model Overview Our approach to the formalization of the actions follows the lines of Description Logic (DL) [2]. We draw on the state-transition action model to develop a theoretical model of the action [5].

For instance, Boger et al. [6] used such approach in the development of the COACH system; a cognitive aide for patients with dementia based on a partially observable Markov decision process (POMDP). g. prompts or reminders). The weakness of the probabilistic approaches stems from the heuristic methods used to compute the probability of each competing hypothesis, which are highly dependent on the context [7]. The learning techniques seek to identify patterns from the observed actions in order to build a probabilistic predictive model of the observed agent behaviour.

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