Explanation Based Learning With Plausible Inferencing
DOWNLOAD
Download Explanation Based Learning With Plausible Inferencing PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Explanation Based Learning With Plausible Inferencing book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Explanation Based Learning With Plausible Inferencing
DOWNLOAD
Author : Gerald DeJong
language : en
Publisher:
Release Date : 1990
Explanation Based Learning With Plausible Inferencing written by Gerald DeJong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.
This paper represents a synthesis of ideas from qualitative reasoning and explanation-based learning. Taken together they form a novel approach to planning that relies on plausible inferencing and applies to continuously varying rather than discrete world states. Interestingly, the frame problem skirted and the approach admits some forms of planning under uncertainty. Planning in a domain is very efficient, although learning about the domain can be time consuming. The approach possess a kind of natural reactivity. Keywords: Explanation based learning, Planning, Learning to plan, Continuous domains, Knowledge level learning. (SDW).
Explanation Based Learning With Plausible Inference
DOWNLOAD
Author : G. DeJong
language : en
Publisher:
Release Date : 1990
Explanation Based Learning With Plausible Inference written by G. DeJong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with categories.
Methodologies For Intelligent Systems
DOWNLOAD
Author : Zbigniew Raâs
language : en
Publisher: Springer Science & Business Media
Release Date : 1991-09-25
Methodologies For Intelligent Systems written by Zbigniew Raâs and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-09-25 with Computers categories.
This volume contains the papers selected for presentation at the Sixth International Symposium on Methodol- ogies for Intelligent Systems held in Charlotte, North Carolina, in October 1991. The symposium was hosted by UNC-Charlotte and sponsored by IBM-Charlotte, ORNL/CESAR and UNC-Charlotte. The papers discuss topics in the following major areas: - Approximate reasoning, - Expert systems, - Intelligent databases, - Knowledge representation, - Learning and adaptive systems, - Logic for artificial intelligence. The goal of the symposium was to provide a platform for a useful exchange and cross-fertilization of ideas between theoreticians and practitioners in these areas.
Machine Learning Methods For Planning
DOWNLOAD
Author : Steven Minton
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-12
Machine Learning Methods For Planning written by Steven Minton and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Social Science categories.
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.
Investigating Explanation Based Learning
DOWNLOAD
Author : Gerald DeJong
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Investigating Explanation Based Learning written by Gerald DeJong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.
Machine Learning
DOWNLOAD
Author : Ryszard S. Michalski
language : en
Publisher: Morgan Kaufmann
Release Date : 1994-02-09
Machine Learning written by Ryszard S. Michalski and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-02-09 with Computers categories.
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
Machine Learning Proceedings 1991
DOWNLOAD
Author : Machine Learning
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28
Machine Learning Proceedings 1991 written by Machine Learning and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Machine Learning
Recent Advances In Robot Learning
DOWNLOAD
Author : Judy A. Franklin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Recent Advances In Robot Learning written by Judy A. Franklin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
Evaluating Explanations
DOWNLOAD
Author : David B. Leake
language : en
Publisher: Psychology Press
Release Date : 2014-02-25
Evaluating Explanations written by David B. Leake and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-25 with Psychology categories.
Psychology and philosophy have long studied the nature and role of explanation. More recently, artificial intelligence research has developed promising theories of how explanation facilitates learning and generalization. By using explanations to guide learning, explanation-based methods allow reliable learning of new concepts in complex situations, often from observing a single example. The author of this volume, however, argues that explanation-based learning research has neglected key issues in explanation construction and evaluation. By examining the issues in the context of a story understanding system that explains novel events in news stories, the author shows that the standard assumptions do not apply to complex real-world domains. An alternative theory is presented, one that demonstrates that context -- involving both explainer beliefs and goals -- is crucial in deciding an explanation's goodness and that a theory of the possible contexts can be used to determine which explanations are appropriate. This important view is demonstrated with examples of the performance of ACCEPTER, a computer system for story understanding, anomaly detection, and explanation evaluation.
Multistrategy Learning
DOWNLOAD
Author : Ryszard S. Michalski
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Multistrategy Learning written by Ryszard S. Michalski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.