By Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan Lingras (eds.)
This publication constitutes the refereed court cases of the twenty fourth convention on synthetic 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 conscientiously reviewed and chosen from eighty one submissions. The papers hide a extensive diversity of issues featuring unique paintings in all components of synthetic intelligence, both theoretical or applied.
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
Rcording location reports discretly introduces uncertainty about the location of MOs between reports. Lowering uncertainty has been addressed by a number of researchers over the past few years [1–4]. These approaches try to ﬁnd a link between the amount of uncertainty and the frequency of location reporting. By increasing the reporting frequency, the uncertainty can be kept within acceptable bounds. We believe that there is a need for a new approach for lowering the uncertainty without increasing the reporting frequency.
Xm ∈ αRm , f (x1 , . . am )=n βR1 (a1 ) · · · βRm (am ) Intuitively, βR (n) is the combination of all possible ways that f can be evaluated as n. (5) t is a term in form of f (t1 , · · · , tm ), where f is an expansion function. 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.
And then, the atomic formulas which have complex terms are rewritten as disjunction or conjunction of atomic formulas in form x < t(¯ y ) and x > t(¯ y ) . The ground solver used by IDP system is an extension of regular SAT solvers which is capable of handling aggregates internally. This enables them to translate specifications and instances into their ground solver input. ”. In this approach, systems with a high-level language helps users a lot and reduces the amount of expertise a user need to have, and thus provides a way of solving computationally hard AI problems to a wider variety of users.