Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan's 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 publication 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 perhaps 2011. The 23 revised complete papers awarded including 22 revised brief papers and five papers from the graduate scholar symposium have been rigorously reviewed and chosen from eighty one submissions. The papers conceal a huge variety of subject matters featuring unique paintings in all components of man-made intelligence, both theoretical or applied.

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Extra 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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Syntax and Semantics of Aggregate Operators. 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.

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. a ¯ contributes to the output of aggregate if B |= φ(¯ a, ¯b). δR1 (γ, α, f (¯ a)) describes the condition under which t1 (¯ a) contributes f (¯ a) to the output. So, for a given mapping from Dx¯ to αR1 , we need to conjunct the conditions obtained from δR1 to find the necessary and sufficient condition for one of the cases where the output sum is exactly n.

P is an expansion predicate and R is an answer to ∃x1 . . xm (x1 = t1 ∧ · · · ∧ xm = tm ∧ P (x1 , . . , xm )) Example 5. (Continue Example 3) Although, ψ does not have any complex term, to demonstrate how the base cases can be handled, the process of computing an answer for ψ is described here. We have computed an answer to t , t. 2}. In this example, {(0, 2, ), (2, 1, )} is an answer to ψ. 3 It is not the purpose of this paper to discuss the techniques we have used in this phase. 22 A. Aavani et al.

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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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