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Tuesday, April 21, 2020 | History

5 edition of Extending explanation-based learning by generalizing the structure of explanations found in the catalog.

Extending explanation-based learning by generalizing the structure of explanations

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  • 25 Currently reading

Published by Pitman, Morgan Kaufmann in London, San Mateo, Calif .
Written in English

    Subjects:
  • Artificial intelligence.,
  • Explanation-based learning.,
  • Comprehension (Theory of knowledge)

  • Edition Notes

    Includes bibliographical references (p.203-219).

    StatementJude W. Shavlik.
    SeriesResearch notes in artificial intelligence,, Research notes in artificial intelligence (London, England)
    Classifications
    LC ClassificationsQ335 .S466 1990
    The Physical Object
    Pagination219 p. :
    Number of Pages219
    ID Numbers
    Open LibraryOL2204998M
    ISBN 100273088173, 1558601090
    LC Control Number89026957


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Extending explanation-based learning by generalizing the structure of explanations by Jude W. Shavlik Download PDF EPUB FB2

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.

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 Book Edition: 1.

Extending explanation-based learning by generalizing the structure of explanations Material Type Book Language English Title Extending explanation-based learning by generalizing the structure of explanations Author(S) Jude W. Shavlik Publication Data London: Pitman Publication€ Date Edition NA Physical Description p Subject Computer.

Extending explanation-based learning by generalizing the structure of explanations. London: Pitman ; San Mateo, Calif.: Morgan Kaufmann, (OCoLC) Document Type: Book: All Authors / Contributors: Jude W Shavlik. Shavlik, Jude W. Extending Explanation-Based Learning by Generalizing the Structure of Explanations Druck-Ausgabe: Material Type: Document, Internet resource: Document Type: Internet Resource, Computer File: All Authors / Contributors: Jude W Shavlik.

We show how disjunctive augmentation (DeJong88) can be achieved by retaining complete explanation structures instead of extracting one single rule out of such a structure.

The proposed algorithm integrates knowledge learned from several examples, a Author: Bernhard Pfahringer. The perspective on explanation-based generalization af- forded by this general method is also used to identify open research problems in this area.

View Show abstract. Explanation Based Learning. The four inputs of a classical Explanation Based Learning system (cL [Mitchell 86],[De Jong 86]) are: • a concept definition of the concept to be operationalized • a training example • a domain theory: a set of roles, describing why the training example is a positive example.

Abstract. The generalization algorithm given in Chapter 2 is not sufficient for all types of generalization.

The EGGS algorithm and the similar EBG algorithm [] are not capable of significantly altering the structure of the eless, such structural alteration is a distinguishing and crucial step in several noteworthy types of by: 1.

An approach to generalizing number in explanation-based learning is presented. Generalizing number can involve generalizing such things as the number of entities involved in a concept or the number of times some action is performed.

This issue has been largely ignored in previous explanation-based learning research. An Explanation-based Learning (EBL) system accepts an example (i.e. a training example) and explains what it learns from the EBL system takes only the relevant aspects of the explanation is translated into particular form that a problem solving program can understand.

The explanation is generalized so that it can be used to solve other problems. Generalizing the Structure of Explanations in Explanation-Based Learning. A number of explanation-based generalization algorithms have been developed. and setting a table can involve an arbitrary number of theories of extending explanations during the generalization process have been developed and computer implementations Author: Jude William Shavlik.

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 Price: $ The structure of explanations renders self-explaining a particularly effective strategy for learning in a way that fosters generalization.

By subsuming study materials under general patterns, explanations highlight the aspects of the explanandum relevant for particular conclusions to follow or solution strategies to be by: Planning and Learning: Explanation-Based Learning Manuela Veloso Carnegie Mellon University Planning, Execution, and Learning Fall Thanks to Daniel Borrajo Learning in Planning Opportunities and improvements along several dimensions: • Search Efficiency: Learn control knowledge to guide the planner through its search space.

er learning b y heuristics, empirical learning, learning b y testing, inductiv e learn-ing, learning b y analogies, and explanation-based learning. Ho w ev er, none of them (except [Sha v90]) generalized the structure of explanations or generalized n um b ers.

An inference rule resulting from generalizing n um b ers subsumes an in nite class of. In fact, ‘explanations’ that merely identifyrelevantprinciplesimprovelearning[53],whereas explanations that do not relate novel information to prior beliefs are less effective [59].

The structure of explanations renders self-explaining a particularly effective strategy for learning in a way that fosters generalization. The influence of explanation type on generalization holds even though all participants are provided with the same mechanistic and functional information, and whether an explanation type is freely generated (Experiment 1).

Explanation-based learning (EBL) is a technique by which an intelligent system can learn by observing examples. EBL systems are characterized by the ability to create justified generalizations from single training : EllmanThomas.

Read Computational Complexity Theory books like Computational complexity theory A Complete Guide and Distributed Algorithms for free with a free day trial.

Extending Explanation-Based Learning by Generalizing the Structure of Explanations. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy.

Send article to Kindle To send this article to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of. Exploring the role of explanation in the context of category learning has two important benefits.

First, there are already reasons, both theoretical and empirical, to suspect an important relationship between explanation and category by: Recent work on generalization suggests that explanations may be valuable in permitting learners to develop generalizations from one or a few examples • We explore this idea by describing four generalization paradigms in which explanations play a part: explanation-based generalization (EBG), structure mapping analogical generalization (SMAG.

When explanation-based learning (EBL) is used for knowledge level learning (KLL), training examples are essential, and EBL is not simply reducible to partial evaluation. A key enabling factor in this behavior is the use of domain theories in which not every element is believed a priori.

Explanation-based learning (EBL) systems attempt to improve the performance of a problem solver (PS), by first examining how PS solved previous problems, then modifying PS to enable it to solve similar problems better (typically, more efficiently) in the article first motivates and explains this explanation-based learning task, then discusses the challenges that any.

The ability to generalize from the known to the unknown is central to learning and inference. Two experiments explore the relationship between how a property is explained and how that property is generalized to novel species and artifacts.

The experiments contrast the consequences of explaining a property mechanistically, by appeal to parts and processes, with Cited by: Extending explanation-based generalization by abstraction operators.

Pages Explanation-based generalization and constraint propagation with interval labels. Pages Zercher, Kai. Preview. Learning by explanation of failures. Pages Urbano, Paulo Book Title Machine Learning - EWSL Book Subtitle European Working.

Explanation and property generalization. Beginning with the influential work of Carey and Osherson et al. (), cognitive scientists have studied property generalization as a window into inductive reasoning.A consistent finding is that property generalization is influenced by the similarity of the categories by: Extending explanation‐based learning by generalizing the structure of explanations / Jude W.

Shavlik. Shavlik, Jude W. QS62 Soar: a cognitive architecture in perspective: a tribute to Allen Newell / edited by John A. Michon and Aladin Akyu¿¿rek. Extending explanation-based learning by generalizing the structure of explanations / Jude W.

Shavlik. Q S Reasoning about change: time and causation from the standpoint of artificial intelligence / Yoav Shaham. Quantitative Results Concerning the Utility of Explanation-Based Learning Steven Minton Computer Science Department1 Carnegie-Mellon University Pittsburgh, PA Abstract Although P revious research has demonstrated that EBL.

Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory in order to make generalizations or form concepts from. Extending Explanation-Based Learning by Generalizing the Structure of Explanations Author Jude W.

Shavlik Summary of AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee | Conversation Starters. Inductive Learning In an Inductive Learning lesson, students examine, group, and label specific "bits" of information to find patterns.

For example, if given 20 specific weather-related terms (e.g., rain, sleet, snow, hygrometer, rain gauge, thermometer, humid, dry, windy, cold), students might group the terms into an initial set of categories.

was proposed for a number of machine learning approaches, including explanation based learning and inductive logic programming. Least general generalization was originally introduced by (Plotkin ). It is the opposite of most general unification (Robinson ) therefore it is also called anti-unification.

Explanation-based learning is a radically different approach that uses existing declarative domain knowledge to "explain" individual examples and uses this explanation to drive a knowledge-based generalization of the example.

It is therefore capable of learning a very general concept from only a single training example. The Kluwer international series in engineering and computer science, SECS Knowledge representation, learning, and expert systems.

The Soar papers: research on integrated intelligence / Author: edited by Paul S. Rosenbloom, John E. Laird, and Allen Newell. STEAC-BOOK: Steacie Stacks Q S63 V.1 Q R86 The use of knowledge in analogy and induction / Stuart J.

Russell. Q R87 Learning with nested generalized exemplars / by Steven L. Salzberg. In explanation-based learning (EBL), domain knowledge is leveraged to learn general rules from few examples.

An explanation is constructed for initial exemplars and then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is by: Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin () as "the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories".More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on.

Strategies for Learning Search Control Rules: An Explanation-based Approach Steven Minton and Jaime G. Carbonell Computer Science Department Carnegie-Mellon University Pittsburgh, PA Abstract Previous work in explanation-based learning has primarily focused on developing problem solvers that learn by observing solutions.Remarks on Lazy and Eager Learning Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Hypothesis Space Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms UNIT IV Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First Order Rules, Learning .Another idea is to base learning on building and generalizing a special type of explanation structure called plausible justification tree which is composed of different types of inference and relates the learner's knowledge to the input.

In this framework, learning consists of extending and/or improving the knowledge base of.