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Read e-book online 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 man made Intelligence, Canadian AI 2011, held in St. John’s, Canada, in could 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 conceal a extensive variety of subject matters featuring unique paintings in all components of man-made intelligence, both theoretical or applied.

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Read or Download Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings PDF

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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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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.

We may use evaluation for formulas with expansion predicates. By evaluating a formula, which has expansion predicates, as true we mean that there is a solution for the whole specification which satisfies the given formula, too. Also, for sake of representation, we may use φ[¯ a, z¯2 ] as a short-hand for φ(z¯1 , z¯2 )[z¯1 /¯ a], which denotes substituting a ¯ for every occurrence of z¯1 in φ. Although our system supports grounding specification having Max, Min, Sum and Count aggregates, but for the sake of space, we just focus on Sum and Count aggregate in this paper: – t(¯ y ) = M axx¯ {t(¯ x, y¯) : φ(¯ x, y¯); dM (¯ y )}, for any instantiation ¯b for y¯, denotes the ¯ maximum value obtained by t[¯ a, b] over all instantiations a ¯ for x ¯ for which φ[¯ a, ¯b] is true, or dM if there is none.

We introduce an expansion predicate Ef (¯ x, y) for each expansion function f (¯ x) where type of y is the same as range of f . am ∈αRm βR1 (a1 ) ··· βRm (am ) Ta1 ,··· ,am ,n Where Ta1 ,··· ,am ,n is an answer to ∃ ∧i xi = ai ∧ y = n ∧ Ef (x1 , · · · , xm , y). βR (n) expresses that f (t1 , · · · , tm ) is equal to n under assignment γ iff ti [γ] = ai and f (a1 , · · · , am ) = n. (6) t is Sumx¯ {t1 (¯ x, y¯) : φ(¯ x, y¯)} : αR = { a¯∈Dx¯ f (¯ a) : f : x¯ → {0} ∪ αR1 }. t. f :¯ x→αR a ¯ ∈D( x ¯) f (¯ a)=n a ¯ ∈Dx ¯ For a fixed instantiation of y¯ (¯b), each instantiation of x ¯ (¯ a), might or might not contribute to the output value of the aggregate when y¯ is equal to ¯b.

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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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