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| Author | : International Joint Conferences on Artificial Intelligence |
| Publisher | : Elsevier |
| Total Pages | : 1368 |
| Release | : 1985 |
| Genre | : Artificial Intelligence |
| ISBN | : 9780934613026 |
| Author | : J.A. Botía |
| Publisher | : IOS Press |
| Total Pages | : 808 |
| Release | : 2013-07-26 |
| Genre | : Computers |
| ISBN | : 161499286X |
Intelligent Environments (IE) play an increasingly important role in many areas of our lives, including education, healthcare and the domestic environment. The term refers to physical spaces incorporating pervasive computing technology used to achieve specific goals for the user, the environment or both. This book presents the proceedings of the workshops of the 9th International Conference on Intelligent Environments (IE ‘13), held in Athens, Greece, in July 2013. The workshops which were presented in the context of this conference range from regular lectures to practical sessions. They provide a forum for scientists, researchers and engineers from both industry and academia to engage in discussions on newly emerging or rapidly evolving topics in the field. Topics covered in the workshops include artificial intelligence techniques for ambient intelligence; applications of affective computing in intelligent environments; smart offices and other workplaces; intelligent environment technology in education for creative learning; museums as intelligent environments; the application of intelligent environment technologies in the urban context for creating more sociable, intelligent cities and for constructing urban intelligence. IE can enrich user experience, better manage the environment’s resources, and increase user awareness of that environment. This book will be of interest to all those whose work involves the application of intelligent environments.
| Author | : Peter Edwards |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 497 |
| Release | : 2014-06-28 |
| Genre | : Computers |
| ISBN | : 1483298531 |
Machine Learning Proceedings 1992
| Author | : Jude W. Shavlik |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 232 |
| Release | : 2014-07-10 |
| Genre | : Computers |
| ISBN | : 1483258912 |
Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning.
| Author | : John R. Josephson |
| Publisher | : Cambridge University Press |
| Total Pages | : 322 |
| Release | : 1996-08-28 |
| Genre | : Computers |
| ISBN | : 9780521575454 |
This book is about abduction, 'the logic of Sherlock Holmes', and about how some kinds of abductive reasoning can be programmed in a computer. The work brings together Artificial Intelligence and philosophy of science and is rich with implications for other areas such as, psychology, medical informatics, and linguistics. It also has subtle implications for evidence evaluation in areas such as accident investigation, confirmation of scientific theories, law, diagnosis, and financial auditing. The book is about certainty and the logico-computational foundations of knowledge; it is about inference in perception, reasoning strategies, and building expert systems.
| Author | : Steven Minton |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 555 |
| Release | : 2014-05-12 |
| Genre | : Social Science |
| ISBN | : 1483221172 |
Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.
| Author | : John Mylopoulos |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 697 |
| Release | : 2014-06-28 |
| Genre | : Computers |
| ISBN | : 0080886620 |
The interaction of database and AI technologies is crucial to such applications as data mining, active databases, and knowledge-based expert systems. This volume collects the primary readings on the interactions, actual and potential, between these two fields. The editors have chosen articles to balance significant early research and the best and most comprehensive articles from the 1980s. An in-depth introduction discusses basic research motivations, giving a survey of the history, concepts, and terminology of the interaction. Major themes, approaches and results, open issues and future directions are all discussed, including the results of a major survey conducted by the editors of current work in industry and research labs. Thirteen sections follow, each with a short introduction. Topics examined include semantic data models with emphasis on conceptual modeling techniques for databases and information systems and the integration of data model concepts in high-level data languages, definition and maintenance of integrity constraints in databases and knowledge bases, natural language front ends, object-oriented database management systems, implementation issues such as concurrency control and error recovery, and representation of time and knowledge incompleteness from the viewpoints of databases, logic programming, and AI.
| Author | : James A. Hendler |
| Publisher | : Psychology Press |
| Total Pages | : 318 |
| Release | : 2014-05-12 |
| Genre | : Psychology |
| ISBN | : 131776661X |
A recent area of interest in the Artificial Intelligence community has been the application of massively parallel algorithms to enhance the choice mechanism in traditional AI problems. This volume provides a detailed description of how marker-passing -- a parallel, non-deductive, spreading activation algorithm -- is a powerful approach to refining the choice mechanisms in an AI problem-solving system. The author scrutinizes the design of both the algorithm and the system, and then reviews the current literature and research in planning and marker passing. Also included: a comparison of this computer model with some standard cognitive models, and a comparison of this model to the "connectionist" approach.
| Author | : Steven Minton |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 217 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461317037 |
The ability to learn from experience is a fundamental requirement for intelligence. One of the most basic characteristics of human intelligence is that people can learn from problem solving, so that they become more adept at solving problems in a given domain as they gain experience. This book investigates how computers may be programmed so that they too can learn from experience. Specifically, the aim is to take a very general, but inefficient, problem solving system and train it on a set of problems from a given domain, so that it can transform itself into a specialized, efficient problem solver for that domain. on a knowledge-intensive Recently there has been considerable progress made learning approach, explanation-based learning (EBL), that brings us closer to this possibility. As demonstrated in this book, EBL can be used to analyze a problem solving episode in order to acquire control knowledge. Control knowledge guides the problem solver's search by indicating the best alternatives to pursue at each choice point. An EBL system can produce domain specific control knowledge by explaining why the choices made during a problem solving episode were, or were not, appropriate.
