This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with...
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ISBN:
(数字)9783540879879
ISBN:
(纸本)9783540879862
This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery science (DS 2008). The technical program of ALT 2008 contained 31 papers selected from 46 submissions, and 5 invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2008 was the 19th in the ALT conference series, established in Japan in 1990. The series Analogical and Inductive Inference is a predecessor of this series: it was held in 1986, 1989 and 1992, co-located with ALT in 1994, and s- sequently merged with ALT. ALT maintains its strong connections to Japan, but has also been held in other countries, such as Australia, Germany, Italy, Sin- pore, Spain and the USA. The ALT conference series is supervised by its Steering Committee: Naoki Abe (IBM T. J.
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume S...
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ISBN:
(数字)9783540349549
ISBN:
(纸本)9783540349532;9783642071164
I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
Foundation of logic historically dates back to the times of Aristotle, who pioneered the concept of truth/falsehood paradigm in reasoning. math.matical logic of propositions and predicates, which are based on the clas...
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ISBN:
(数字)9783540334590
ISBN:
(纸本)9783540334583;9783642070082
Foundation of logic historically dates back to the times of Aristotle, who pioneered the concept of truth/falsehood paradigm in reasoning. math.matical logic of propositions and predicates, which are based on the classical models of Aristotle, underwent a dramatic evolution during the last 50 years for its increasing applications in automated reasoning on digital computers. The subject of Logic Programming is concerned with automated reasoning with facts and knowledge to answer a user’s query following the syntax and semantics of the logic of propositions/predicates. The credit of automated reasoning by logic programs goes to Professor Robinson for his well-known resolution theorem that provides a general scheme to select two program clauses for deriving an inference. Until now Robinson’s theorem is being used in PROLOG/DATALOG compilers to automatically build a Select Linear Definite (SLD) clause based resolution tree for answering a user’s query. The SLD-tree based scheme for reasoning undoubtedly opened a new era in logic programming for its simplicity in implementation in the compilers. In fact, SLD-tree construction suffices the need for users with a limited set of program clauses. But with increase in the number of program clauses, the execution time of the program also increases linearly by the SLD-tree based approach. An inspection of a large number of logic programs, however, reveals that more than one pair of program clauses can be resolved simultaneously without violating the syntax and the semantics of logic programming. This book employs this principle to speed up the execution time of logic programs.
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