Download e-book for iPad: Advances in Artificial Intelligence: 24th Canadian by Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan

By Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan Lingras (eds.)

ISBN-10: 3642210422

ISBN-13: 9783642210426

This e-book constitutes the refereed court cases of the twenty fourth convention on man made Intelligence, Canadian AI 2011, held in St. John’s, Canada, in may well 2011. The 23 revised complete papers awarded including 22 revised brief papers and five papers from the graduate scholar symposium have been conscientiously reviewed and chosen from eighty one submissions. The papers conceal a wide variety of themes featuring unique paintings in all components of synthetic intelligence, both theoretical or applied.

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Additional info for Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings

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Cambridge University Press, New York (2009) 7. : Probabilistic inference using belief networks is np-hard. Technical report SMI-87-0195, Knowledge Systems Laboratory, Stanford University, Stanford, CA, USA (1987) 8. : Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29(3), 241–288 (1986) 9. : A tutorial on learning with bayesian networks. , Seattle, Washington (1995) 10. : Learning Bayesian Networks. uk 2 NLP Technologies Inc. ca Abstract. In this paper, we investigate an unsupervised approach to Relation Extraction to be applied in the context of automatic generation of multiplechoice questions (MCQs).

DX = Ak . 20 A. Aavani et al. Proposition 3 (Answers to Terms). Let R be the pair (αR , βR ), and t a term . Assume that t1 , . . tm are terms, and R1 , . . Rm (respectively) are answers to those terms wrt A. Also, let S be an answer for φ. , involves only variables, instance functions, and arithmetic operators): αR = {n ∈ N | ∃ a ∈ DX : (t[a] = n)} and for all n ∈ αR , βR (n) is an answer to t = n computed as described in Definition (6). (2) t is a term in form of t1 + t2 : αR = {x + y | x ∈ αR1 and y ∈ αR2 } βR (n) = ∪(j∈αR1 , k∈αR2 , n=j+k) βR1 (j) βR2 (k) (3) t is a term in form of t1 {−, ×}t2: similar to case (2); (4) t is a term in form of f (t1 , · · · , tm ), where f is an instance function: αR = {y| for some x1 ∈ αR1 , .

For previous movements of the MOs, the uncertainty stems from the discrete nature of location tracking systems, where gaps are created among the location reports. Future locations of MOs are, by definition, uncertain. The objective is to maximize the estimation accuracy while minimizing the operating costs. Keywords: Moving object modelling, Managing uncertainty, Location tracking Systems. 1 Introduction Location Tracking Systems (LTSs) are built to answer queries about the whereabouts of moving objects in the past, present, or future.

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Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings by Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan Lingras (eds.)


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