Last edited by Zulkile
Sunday, July 26, 2020 | History

1 edition of Intelligent Learning Environments and Knowledge Acquisition in Physics found in the catalog.

Intelligent Learning Environments and Knowledge Acquisition in Physics

by AndrГ©e Tiberghien

  • 125 Want to read
  • 20 Currently reading

Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

    Subjects:
  • Computer science,
  • Artificial intelligence

  • Edition Notes

    Other titlesProceedings of the NATO Advanced Research Workshop on Knowledge Acquisition in the Domain of Physics and Intelligent Learning Environments, held in Lyon, France, July 8-12, 1990
    Statementedited by Andrée Tiberghien, Heinz Mandl
    SeriesNATO ASI Series, Series F: Computer and Systems Science -- 86, NATO ASI Series, Series F: Computer and Systems Science -- 86
    ContributionsMandl, Heinz
    Classifications
    LC ClassificationsQ334-342, TJ210.2-211.495
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (viii, 283p. 67 illus.)
    Number of Pages283
    ID Numbers
    Open LibraryOL27046155M
    ISBN 103642847862, 3642847846
    ISBN 109783642847868, 9783642847844
    OCLC/WorldCa851378407

      Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology. environment will be known in great detail to the designer, for example, the size and weight of the robot, the charac-teristics of its sensors and effectors, and at least some of the physics of the task environment. Our first principle guiding the cooperative knowledge acquisition effort is: 1. The robot needs all the help it can get.

    The book is divided into three sections: applications of cognitive research to teaching specific content areas; applications for learning across the curriculum; and applications that challenge traditional concepts of classroom-based learning environments. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new.

    Evolution of Knowledge Science: Myth to Medicine: Intelligent Internet-Based Humanist Machines explains how to design and build the next generation of intelligent machines that solve social and environmental problems in a systematic, coherent, and optimal fashion. The book brings together principles from computer and communication sciences, electrical engineering, mathematics, physics, . Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined. The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience.


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Intelligent Learning Environments and Knowledge Acquisition in Physics by AndrГ©e Tiberghien Download PDF EPUB FB2

This book presents the proceedings of the NATO workshop "Knowledgeacquisition in the domain of physics and intelligent learning environments" held in Lyon, France, in July The contributions show clearly the different research approaches used in the area: via artificialintelligence, cognitive psychology, and physics education (didactics).

Introduction The NATO workshop ''Knowledge acquisition in the domain of physics and intelligent learning environments" was held in Lyon, France, JulyA total of 31 researchers from Europe (France, Germany, Greece, Italy, Portugal, and the U. K.), the U. A., and Japan worked together. This book presents the proceedings of the NATO workshop "Knowledge acquisition in the domain of physics and intelligent learning environments" held in Lyon, France, in July The contributions show clearly the different research approaches used in the area: via artificial intelligence, cognitive psychology, and physics education (didactics).

"Proceedings of the NATO Advanced Research Workshop on Knowledge Acquisition in the Domain of Physics and Intelligent Learning Environments, held in Lyon, France, July"--Title page verso. Description: viii, pages: illustrations ; 25 cm. Contents: 1. Teaching Situations and Physics Knowledge   COVID campus closures: see options for getting or retaining Remote Access to subscribed contentCited by: 3.

Borghi, L. () Computer simulation of historical experiments and understanding of physics concept. In A. Tiberghien and H. Mandi (eds) Intelligent learning environments and knowledge acquisition in physics, – Berlin: Springer Google Scholar Brousseau, G.

() Le contrat didactique, le milieu. The main aim of this research book is to report a sample of the most recent advances in the field of intelligent interactive systems in knowledge-based environment.

The contents of this book include: Introduction to intelligent interactive systems; Affective bi-modal intelligent tutoring system; Estimation of development costs in intelligent systems; Narrative interactive learning; Knowledge acquisition. intelligent autonomous air combat from the aspects of air-to-air knowledge acquisition and self-learning, intelligent air combat decision-making, autonomous integrated control and technical verification methods, and proposed flight training and simulation training.

Air combat experience and. Chapters consider explicit models of knowledge with corresponding instruction designed to enable learners to build on that knowledge, acquisition of specified knowledge, and what knowledge is useful in contemporary curricula.

Contributors: Kate McGilly. Sharon A. Griffin, Robbie Case, and Robert S. Siegler. Earl Hunt and Jim Minstrell. POPOVIC, in Soft Computing and Intelligent Systems, Knowledge Acquisition.

Knowledge acquisition is an activity of knowledge engineering that is very important in the initial phase of system shaping for building the fundamental knowledge base, as well as in the application phase of the system for knowledge base updating [8].To the domain knowledge to be initially acquired also.

This study investigates the knowledge acquisition of biology and physics freshmen students with special regard to differences between high school (HS) high performing and low performing students. Our study is based on a prior knowledge model, which describes explicit knowledge as a composite of four knowledge types: knowledge of facts, knowledge of meaning, integration of knowledge, and.

Issues connected with knowledge acquisition through the development of complex computer programs--Intelligent Tutoring Systems (ITSs)--are discussed. The components of such a system and its applications are considered, including: elicitation of knowledge, tutoring with incomplete/uncertain knowledge, self-improving tutoring systems, and limitations of ITSs.

Such knowledge and experience can lead to a high level of expertise in one or more fields. People who live in "rich" learning environments have a significant intelligence advantage over people who grow up in less stimulating environments.

Experiential intelligence can be increased by such environments. Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications.

Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Knowledge acquisition in complex and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing.

Intelligent agents often have the same or similar tasks and sometimes they cooperate to solve a given problem. These agents typically know how to observe their local environment and how to react on.

A Cognitive Approach to the Teaching of Physics. Earl Hunt and Jim Minstrell. PDF ( MB) 4. Enhancing the Acquisition of Conceptual Structures through Hypermedia. Kathryn T. Spoehr. PDF ( MB) 5. Intelligence in Context: Enhancing Students' Practical Intelligence for School.

Howard Gadner, Mara Krechevsky, Robert J. Sternberg and Lynn. Intelligent learning environments (ILEs) can be designed to support the development of learners cognitive skills, strategies, and metacognitive processes as they work on complex decision-making.

Advanced knowledge acquisition in a subject area is different in many important ways from introductory learning (and from expertise). In this paper we discuss some of the special characteristics of advanced learning of complex conceptual material.

We note how these characteristics are often at odds with the goals and tactics of introductory instruction and with psychological biases in learning.

Keywords: Expert Systems, Knowledge-Based Systems, Artificial Intelligence, Knowledge Acquisition Contents 1. Introduction 2. General Knowledge representation for design purposes 3. The Knowledge Acquisition Problem Acquisition of Knowledge is a formidable Problem in itself Implementing the Knowledge Base Qualitative Knowledge.

An intelligent learning environment is a relatively new kind of intelligent educational system which combines the features of traditional Intelligent Tutoring Systems (ITS) and learning environments. Traditional ITS are able to support and control student's learning on several levels but doesn't provide space for student-driven learning and.Knowledge of real-worlds plays a vital role in intelligence and same for creating artificial intelligence.

Knowledge plays an important role in demonstrating intelligent behavior in AI agents. An agent is only able to accurately act on some input when he has some knowledge or experience about that input.The aim of research into Knowledge-Based and Intelligent Engineering is to develop systems that replicate the analytical, problem solving and learning capabilities of the brain.

These systems bring the benefits of knowledge and intelligence to the solution of complex problems.