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Machine Learning of Robot Assembly Plans
The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the signific...
Privacy in Statistical Databases
This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2006, held in December 2006 in Rome, Italy. The 31 revised full papers are organized in topical sections on methods for tabular protection, utility and risk in tabular protection, methods for microdata protection, utility and risk in microdata protection, protocols for private computation, case studies, and software.
Investigating Explanation-Based Learning
Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism.
Automated Deduction, Cade-12.
This volume contains the reviewed papers presented at the 12th International Conference on Automated Deduction (CADE-12) held at Nancy, France in June/July 1994. The 67 papers presented were selected from 177 submissions and document many of the most important research results in automated deduction since CADE-11 was held in June 1992. The volume is organized in chapters on heuristics, resolution systems, induction, controlling resolutions, ATP problems, unification, LP applications, special-purpose provers, rewrite rule termination, ATP efficiency, AC unification, higher-order theorem proving, natural systems, problem sets, and system descriptions.
Machine Learning
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Ea...
IJCAI-97
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Diffy-S
Abstract: "The research presented here focuses on inductively acquiring new knowledge for robots. The thesis introduces, DIFFY-S, a system that learns the behavior of robot operators from examples of their observed or desired effects. The outputs of DIFFY-S are hypothesis operators that model these effects. This model can be used to predict the results of a sequence of robot actions, and thus, is useful to a robot that wishes to plan its actions intelligently. The complexity of the knowledge that can be inductive [sic] acquired -- in the form of nested functional expressions -- exceeds that of extant systems for operator learning.
Machine Learning Proceedings 1992
Machine Learning Proceedings 1992
Machine Learning
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