Get Advances in Artificial Intelligence: 24th Canadian PDF

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

ISBN-10: 3642210422

ISBN-13: 9783642210426

This booklet constitutes the refereed court cases of the twenty fourth convention on synthetic Intelligence, Canadian AI 2011, held in St. John’s, Canada, in may perhaps 2011. The 23 revised complete papers offered including 22 revised brief papers and five papers from the graduate pupil symposium have been rigorously reviewed and chosen from eighty one submissions. The papers disguise a large diversity of themes providing unique paintings in all components of man-made intelligence, both theoretical or applied.

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Additional resources 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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Canadian AI 2011, LNAI 6657, pp. 26–31, 2011. c Springer-Verlag Berlin Heidelberg 2011 MO Modelling Approach for Lowering Uncertainty in LTSs 27 We propose a MO modelling approach for lowering the uncertainty about MO locations in a LTS. , the context) under which MOs operate. The MO model is used to reach a more accurate estimate of the MO location. , speed and route) in terms of the MO’s historical data, collected for these parameters under similar circumstances. 2 Moving Object Modelling The goal is to equip location-tracking systems with an MO model for each of the MOs being tracked.

For domain A, the domain of structure A, we denote the set of ˜ In practice, the ground formula should contain no occurrences of such constants by A. the instance vocabulary, in which case we call it reduced. Definition 2 (Reduced Grounding for MX). Formula ψ is a reduced grounding of formula φ over σ-structure A = (A; σ A ) if ˜ and 1 ψ is a ground formula over ε ∪ A, 2 for every expansion structure B = (A; σ A , εB ) over σ ∪ ε, B |= φ iff (B, A˜B ) |= ψ, ˜ where A˜B is the standard interpretation of the new constants A.

98. Ios Pr. , Amsterdam (2006) 4. : Grounding for model expansion in k-guarded formulas with inductive definitions. In: Proc. IJCAI 2007, pp. 161–166 (2007) 5. : A method for solving NP search based on model expansion and grounding. Master’s thesis, Simon Fraser University (2006) 6. : On the complexity of derivation in propositional calculus. Studies in constructive mathematics and mathematical logic 2(115-125), 10–13 (1968) 7. : Efficient haplotype inference with boolean satisfiability. In: AAAI.

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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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