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Learning Search Control Knowledge
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Book Synopsis Learning Search Control Knowledge by : Steven Minton
Download or read book Learning Search Control Knowledge written by Steven Minton and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning Search Control Knowledge for Equational Deduction by : S. A. Schulz
Download or read book Learning Search Control Knowledge for Equational Deduction written by S. A. Schulz and published by IOS Press. This book was released on 2000 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an approach to learning good search guiding heuristics for the supposition-based theorom prover E in equational deductions. Search decisions from successful proof searches are represented as sets annotated clause patterns. Term Space Mapping, an alternative learning method for recursive structures is used to learn heuristic evaluation functions for the evaluation of potential new consequences. Experimental results with extended system E/TSM show the success of the approach. Additional contributions of the thesis are an extended superposition calculus and a description of both the proof procedure and the implementation of a state-of-the-art equational theorem prover.
Book Synopsis Learning Effective Search Control Knowledge by : Steven Minton
Download or read book Learning Effective Search Control Knowledge written by Steven Minton and published by . This book was released on 1988 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Search Control Knowledge to Improve Plan Quality by : M. Alicia Pérez
Download or read book Learning Search Control Knowledge to Improve Plan Quality written by M. Alicia Pérez and published by . This book was released on 1995 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Generating good, production-quality plans is an essential element in transforming planners from research tools into real- world applications, but one that has been frequently overlooked in research on machine learning for planning. Most work has aimed at improving the efficiency of planning ('speed-up learning') or at acquiring or refining the planner's action model. This thesis focuses on learning search-control knowledge to improve the quality of the plans produced by the planner. Knowledge about plan quality in a domain comes in two forms: (a) a post- facto quality metric that computes the quality (e.g. execution cost) of a plan, and (b) planning-time decision-control knowledge used to guide the planner towards high-quality plans. The first kind is not operational until after a plan is produced, but is exactly the kind typically available, in contrast to the far more complex operational decision-time knowledge. Learning operational quality control knowledge can be seen as translating the domain knowledge and quality metrics into runtime decision guidance. The full automation of this mapping based on planning experience is the ultimate objective of this thesis. Given a domain theory, a domain-specific metric of plan quality, and problems which provide planning experience, the Quality architecture developed in this thesis automatically acquires operational control knowledge that effectively improves the quality of the plans generated. Quality can (optionally) learn from human experts who suggest improvements to the plans at the operator (plan step) level. We have designed two distinct domain- independent learning mechanisms to efficiently acquire quality control knowledge. They differ in the language used to represent the learned knowledge, namely control rules and control knowledge trees, and in the kinds of quality metrics for which they are best suited. Quality is fully implemented on top of the Prodigy4.0 nonlinear planner. Its empirical evaluation has shown that the learned knowledge produces near-optimal plans (reducing before-learning plan execution costs 8% to 96%). Although the learning mechanisms and learned knowledge representations have been developed for Prodigy4.0, the framework is general and addresses a problem that must be confronted by any planner that treats planning as a constructive decision-making process."
Book Synopsis Learning Search Control Knowledge for the Deep Space Network Scheduling Problem by : Jonathan Matthew Gratch
Download or read book Learning Search Control Knowledge for the Deep Space Network Scheduling Problem written by Jonathan Matthew Gratch and published by . This book was released on 1993 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis LEARNING SEARCH CONTROL KNOWLEDGE FOR PLANNING WITH CONJUNCTIVE GOALS. by : KWANG RYEL RYU
Download or read book LEARNING SEARCH CONTROL KNOWLEDGE FOR PLANNING WITH CONJUNCTIVE GOALS. written by KWANG RYEL RYU and published by . This book was released on 1992 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: learning correct rules. The overhead involved in learning is very low because this methodology needs only a small amount of data to learn from, namely, the goal stacks from the leaf nodes of a failure search tree, rather than the whole search tree. Empirical tests show that the rules derived by our system PAL, after sufficient learning, performs as well as, and in some cases better than, those derived by other systems such as PRODIGY/EBL and STATIC.
Book Synopsis Constraining Learning with Search Control by : University of Southern California. Information Sciences Institute
Download or read book Constraining Learning with Search Control written by University of Southern California. Information Sciences Institute and published by . This book was released on 1993 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: By making the learning mechanism sensitive to the control knowledge utilized during the problem solving that led to the creation of the new rule -- i.e., by incorporating such control knowledge into the explanation -- the cost of using the learned rule becomes bounded by the cost of the problem solving from which it was learned."
Book Synopsis A Framework for Evaluating Search Control Strategies by : Jonathan Matthew Gratch
Download or read book A Framework for Evaluating Search Control Strategies written by Jonathan Matthew Gratch and published by . This book was released on 1990 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is also clear that these systems make strong assumptions about the topography of the search space, like guaranteed ascent, which we argue are violated. While our focus is on learning control strategies, the issues are relevant to the study of control knowledge in general."
Book Synopsis Encyclopedia of Artificial Intelligence by : Juan Ramon Rabunal
Download or read book Encyclopedia of Artificial Intelligence written by Juan Ramon Rabunal and published by IGI Global. This book was released on 2009-01-01 with total page 1640 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.
Book Synopsis Constraining Learning with Search Control by : University of Southern California. Information Sciences Institute
Download or read book Constraining Learning with Search Control written by University of Southern California. Information Sciences Institute and published by . This book was released on 1993 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: By making the learning mechanism sensitive to the control knowledge utilized during the problem solving that led to the creation of the new rule -- i.e., by incorporating such control knowledge into the explanation -- the cost of using the learned rule becomes bounded by the cost of the problem solving from which it was learned."
Book Synopsis Machine Learning Proceedings 1989 by : Alberto Maria Segre
Download or read book Machine Learning Proceedings 1989 written by Alberto Maria Segre and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1989
Book Synopsis Explanation-Based Neural Network Learning by : Sebastian Thrun
Download or read book Explanation-Based Neural Network Learning written by Sebastian Thrun and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Book Synopsis Machine Learning Proceedings 1991 by : Lawrence A. Birnbaum
Download or read book Machine Learning Proceedings 1991 written by Lawrence A. Birnbaum and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning
Book Synopsis Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques by : Olivas, Emilio Soria
Download or read book Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques written by Olivas, Emilio Soria and published by IGI Global. This book was released on 2009-08-31 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.
Author :Mr. Y. David Solomon Raju, M. Tech, (Ph. D.), LMISTE, LMISOI, FIETE, MIE, MIAENG, Associate Professor, Department of Electronics and Communication Engineering, Holy Mary Institute of Technology & Science (AUTONOMOUS) Publisher :GCS PUBLISHERS ISBN 13 :9394304258 Total Pages : pages Book Rating :4.3/5 (943 download)
Book Synopsis Machine Learning by : Mr. Y. David Solomon Raju, M. Tech, (Ph. D.), LMISTE, LMISOI, FIETE, MIE, MIAENG, Associate Professor, Department of Electronics and Communication Engineering, Holy Mary Institute of Technology & Science (AUTONOMOUS)
Download or read book Machine Learning written by Mr. Y. David Solomon Raju, M. Tech, (Ph. D.), LMISTE, LMISOI, FIETE, MIE, MIAENG, Associate Professor, Department of Electronics and Communication Engineering, Holy Mary Institute of Technology & Science (AUTONOMOUS) and published by GCS PUBLISHERS. This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning WRITTEN BY Y. David Solomon Raju, K. Shyamala, Ch. Sumalatha
Book Synopsis Foundations of Knowledge Acquisition: Machine learning by : Susan F. Chipman
Download or read book Foundations of Knowledge Acquisition: Machine learning written by Susan F. Chipman and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Acquiring Search-control Knowledge Via Static Analysis by : Oren Etzioni
Download or read book Acquiring Search-control Knowledge Via Static Analysis written by Oren Etzioni and published by . This book was released on 1992 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Explanation-Based Learning (EBL) is a widely-used technique for acquiring search-control knowledge. Recently, Prieditis, van Harmelen, and Bundy pointed to the similarity between Partial Evaluation (PE) and EBL. However, EBL utilizes training examples whereas PE does not. It is natural to inquire, therefore, whether PE can be used to acquire search-control knowledge, and if so at what cost? This paper answers these questions by means of a case study comparing PRODIGY/EBL, a state-of-the- art EBL system, and STATIC, a PE-based analyzer of problem-space definitions. When tested in PRODIGY/EBL's benchmark problem spaces, STATIC generated search-control knowledge that was up to three times as effective as the knowledge learned by PRODIGY/EBL, and did so from twenty-six to twenty-seven times faster. The paper describes STATIC's algorithms, and compares its performance to PRODIGY/EBL's, noting when STATIC's superior performance will scale up and when it will not. The paper concludes with several lessons for the design of EBL systems, suggesting hybrid PE/EBL systems as a promising direction for future research."
