Twelfth Conference on Innovative Applications of Artificial IntelligenceAAAI proceedings describe innovative concepts, techniques, perspectives, and observations that present promising research directions in artificial intelligence. The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. Distributed for AAAI Press. |
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Page 182
... Golomb ruler 50 - fold . Introduction In an invited talk at AAAI - 98 , Gene Freuder identified modelling as a major hurdle preventing the uptake of constraint satisfaction technology . Modelling is espe- cially challenging when using ...
... Golomb ruler 50 - fold . Introduction In an invited talk at AAAI - 98 , Gene Freuder identified modelling as a major hurdle preventing the uptake of constraint satisfaction technology . Modelling is espe- cially challenging when using ...
Page 183
... Golomb ruler with x1 = { 0 } , x2 = { 1,2 } , and x3 = { 4 } . The ternary and all - different model is GAC , but the quaternary model is not GAC ( the value 2 must be removed from the domain of x2 ) . Consider a Golomb ruler with r1 ...
... Golomb ruler with x1 = { 0 } , x2 = { 1,2 } , and x3 = { 4 } . The ternary and all - different model is GAC , but the quaternary model is not GAC ( the value 2 must be removed from the domain of x2 ) . Consider a Golomb ruler with r1 ...
Page 186
... Golomb ruler , we can use all the implied constraints introduced in the last section . However , now they have very ... Golomb rulers with two layers , each with m marks , with different vari- able orderings . In all cases , the search ...
... Golomb ruler , we can use all the implied constraints introduced in the last section . However , now they have very ... Golomb rulers with two layers , each with m marks , with different vari- able orderings . In all cases , the search ...
Contents
InterLayer Learning Towards Emergent Cooperative Behavior3 | 3 |
Shawn Arseneau Wei Sun Changpeng Zhao and Jeremy R Cooperstock McGill University | 9 |
Bell NASA Ames Research Center W A Sethares and J A Bucklew University of WisconsinMadison | 15 |
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action agent algorithm allocation applied approach Artificial Intelligence assignment Association for Artificial axioms behavior bids bundle clauses Cobot combinatorial auctions complexity compute compute minimal consider consistent constraints Datalog defeasible logics defined Definition denote domain dynamic equivalent evaluation example Figure Fluent Calculus formula framework function given goal Golog Golomb ruler graph GSAT heuristic implementation inference input instances interaction knowledge base LambdaMOO language learning Lemma Levesque literal local search logic logic programming maximal method minimal models node NP-complete objects ontology optimal paper performance Player prediction problem Proc procedure propositional propositional logic pruning query reasoning relations representation robot rules Sandholm satisfiability Selman semantics sequence situation calculus solution solve specific step strategy structure subset techniques Theorem theory tion tractable tree utility V₁ variables WalkSAT www.aaai.org