Goal-driven learning /:
In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic proce...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©1995.
©1995 |
| Schriftenreihe: | Bradford book.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book. |
| Beschreibung: | "A Bradford book." |
| Beschreibung: | 1 online resource (xxii, 507 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 0585038538 9780585038537 9780262181655 0262181657 0262282046 9780262282048 |
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | 0 | |g 1. |t Learning, goals, and learning goals / |r by Ashwin Ram and David B. Leake -- |g 2. |t Planning to learn / |r by Lawrence Hunter -- |g 3. |t Quantitative results concerning the utility of explanation-based learning / |r by Steven Minton -- |g 4. |t The use of explicit goals for knowledge to guide inference and learning / |r by Ashwin Ram and Lawrence Hunter -- |g 5. |t Deriving categories to achieve goals / |r by Lawrence W. Barsalou -- |g 6. |t Harpoons and long sticks : the interaction of theory and similarity in rule induction / |r by Edward J. Wisniewski and Douglas L. Medin -- |g 7. |t Introspective reasoning using meta-explanations for multistrategy learning / |r by Ashwin Ram and Michael T. Cox -- |g 8. |t Goal-directed learning : a decision-theoretic model for deciding what to learn next / |r by Marie desJardins -- |g 9. |t Goal-based explanation evaluation / |r by David B. Leake -- |g 10. |t Planning to perceive / |r by Louise Pryor and Gregg Collins -- |g 11. |t Planning and learning in PRODIGY : overview of an integrated architecture / |r by Jaime Carboneil [and others] -- |g 12. |t A learning model for the selection of problem-solving strategies in continuous physical systems / |r by Xiaodong Xia and Dif-Yan Yeung -- |g 13. |t Explicitly biased generalization / |r by Diana Gordon and Donald Perlis -- |g 14. |t Three levels of goal orientation in learning / |r by Evelyn Ng and Carl Bereiter -- |g 15. |t Characterizing the application of computer simulations in education : instructional criteria / |r by Jos J.A. van Berkum [and others] -- |g 16. |t Goal-driven learning : fundamental issues and symposium report / |r by David B. Leake and Ashwin Ram -- |g 17. |t Storage side effects : studying processing to understand learning / |r by Lawrence W. Barsalou -- |g 18. |t Goal-driven learning in multistrategy reasoning and learning systems / |r by Ashwin Ram, Michael T. Cox and S. Narayanan -- |g 19. |t Inference to the best plan : a coherence theory of decision / |r by Paul Thagard and Elija Millgram -- |g 20. |t Toward goal-driven integration of explanation and action / |r by David B. Leake -- |g 21. |t Learning as goal-driven inference / |r by Ryszard Michalski and Ashwin Ram. |
| 546 | |a English. | ||
| 520 | |a In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book. | ||
| 650 | 0 | |a Computational learning theory. |0 http://id.loc.gov/authorities/subjects/sh94004662 | |
| 650 | 6 | |a Théorie de l'apprentissage informatique. | |
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| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
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| 700 | 1 | |a Ram, Ashwin. | |
| 700 | 1 | |a Leake, David B. | |
| 758 | |i has work: |a Goal-Driven Learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCY4H44WhGfWxK9C3jmy9Kq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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Datensatz im Suchindex
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| _version_ | 1816796893818126336 |
| adam_text | |
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| author2 | Ram, Ashwin Leake, David B. |
| author2_role | |
| author2_variant | a r ar d b l db dbl |
| author_additional | by Ashwin Ram and David B. Leake -- by Lawrence Hunter -- by Steven Minton -- by Ashwin Ram and Lawrence Hunter -- by Lawrence W. Barsalou -- by Edward J. Wisniewski and Douglas L. Medin -- by Ashwin Ram and Michael T. Cox -- by Marie desJardins -- by David B. Leake -- by Louise Pryor and Gregg Collins -- by Jaime Carboneil [and others] -- by Xiaodong Xia and Dif-Yan Yeung -- by Diana Gordon and Donald Perlis -- by Evelyn Ng and Carl Bereiter -- by Jos J.A. van Berkum [and others] -- by David B. Leake and Ashwin Ram -- by Ashwin Ram, Michael T. Cox and S. Narayanan -- by Paul Thagard and Elija Millgram -- by Ryszard Michalski and Ashwin Ram. |
| author_facet | Ram, Ashwin Leake, David B. |
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| callnumber-sort | Q 3325.7 G63 41995EB |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBU |
| contents | Learning, goals, and learning goals / Planning to learn / Quantitative results concerning the utility of explanation-based learning / The use of explicit goals for knowledge to guide inference and learning / Deriving categories to achieve goals / Harpoons and long sticks : the interaction of theory and similarity in rule induction / Introspective reasoning using meta-explanations for multistrategy learning / Goal-directed learning : a decision-theoretic model for deciding what to learn next / Goal-based explanation evaluation / Planning to perceive / Planning and learning in PRODIGY : overview of an integrated architecture / A learning model for the selection of problem-solving strategies in continuous physical systems / Explicitly biased generalization / Three levels of goal orientation in learning / Characterizing the application of computer simulations in education : instructional criteria / Goal-driven learning : fundamental issues and symposium report / Storage side effects : studying processing to understand learning / Goal-driven learning in multistrategy reasoning and learning systems / Inference to the best plan : a coherence theory of decision / Toward goal-driven integration of explanation and action / Learning as goal-driven inference / |
| ctrlnum | (OCoLC)42636424 |
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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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Leake --</subfield><subfield code="g">10.</subfield><subfield code="t">Planning to perceive /</subfield><subfield code="r">by Louise Pryor and Gregg Collins --</subfield><subfield code="g">11.</subfield><subfield code="t">Planning and learning in PRODIGY : overview of an integrated architecture /</subfield><subfield code="r">by Jaime Carboneil [and others] --</subfield><subfield code="g">12.</subfield><subfield code="t">A learning model for the selection of problem-solving strategies in continuous physical systems /</subfield><subfield code="r">by Xiaodong Xia and Dif-Yan Yeung --</subfield><subfield code="g">13.</subfield><subfield code="t">Explicitly biased generalization /</subfield><subfield code="r">by Diana Gordon and Donald Perlis --</subfield><subfield code="g">14.</subfield><subfield code="t">Three levels of goal orientation in learning /</subfield><subfield code="r">by Evelyn Ng and Carl Bereiter --</subfield><subfield code="g">15.</subfield><subfield code="t">Characterizing the application of computer simulations in education : instructional criteria /</subfield><subfield code="r">by Jos J.A. van Berkum [and others] --</subfield><subfield code="g">16.</subfield><subfield code="t">Goal-driven learning : fundamental issues and symposium report /</subfield><subfield code="r">by David B. Leake and Ashwin Ram --</subfield><subfield code="g">17.</subfield><subfield code="t">Storage side effects : studying processing to understand learning /</subfield><subfield code="r">by Lawrence W. Barsalou --</subfield><subfield code="g">18.</subfield><subfield code="t">Goal-driven learning in multistrategy reasoning and learning systems /</subfield><subfield code="r">by Ashwin Ram, Michael T. Cox and S. Narayanan --</subfield><subfield code="g">19.</subfield><subfield code="t">Inference to the best plan : a coherence theory of decision /</subfield><subfield code="r">by Paul Thagard and Elija Millgram --</subfield><subfield code="g">20.</subfield><subfield code="t">Toward goal-driven integration of explanation and action /</subfield><subfield code="r">by David B. Leake --</subfield><subfield code="g">21.</subfield><subfield code="t">Learning as goal-driven inference /</subfield><subfield code="r">by Ryszard Michalski and Ashwin Ram.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. 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| id | ZDB-4-EBU-ocm42636424 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:48:55Z |
| institution | BVB |
| isbn | 0585038538 9780585038537 9780262181655 0262181657 0262282046 9780262282048 |
| language | English |
| oclc_num | 42636424 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xxii, 507 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | MIT Press, |
| record_format | marc |
| series | Bradford book. |
| spelling | Goal-driven learning / edited by Ashwin Ram and David B. Leake. Cambridge, Mass. : MIT Press, ©1995. ©1995 1 online resource (xxii, 507 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier "A Bradford book." Includes bibliographical references and index. Print version record. 1. Learning, goals, and learning goals / by Ashwin Ram and David B. Leake -- 2. Planning to learn / by Lawrence Hunter -- 3. Quantitative results concerning the utility of explanation-based learning / by Steven Minton -- 4. The use of explicit goals for knowledge to guide inference and learning / by Ashwin Ram and Lawrence Hunter -- 5. Deriving categories to achieve goals / by Lawrence W. Barsalou -- 6. Harpoons and long sticks : the interaction of theory and similarity in rule induction / by Edward J. Wisniewski and Douglas L. Medin -- 7. Introspective reasoning using meta-explanations for multistrategy learning / by Ashwin Ram and Michael T. Cox -- 8. Goal-directed learning : a decision-theoretic model for deciding what to learn next / by Marie desJardins -- 9. Goal-based explanation evaluation / by David B. Leake -- 10. Planning to perceive / by Louise Pryor and Gregg Collins -- 11. Planning and learning in PRODIGY : overview of an integrated architecture / by Jaime Carboneil [and others] -- 12. A learning model for the selection of problem-solving strategies in continuous physical systems / by Xiaodong Xia and Dif-Yan Yeung -- 13. Explicitly biased generalization / by Diana Gordon and Donald Perlis -- 14. Three levels of goal orientation in learning / by Evelyn Ng and Carl Bereiter -- 15. Characterizing the application of computer simulations in education : instructional criteria / by Jos J.A. van Berkum [and others] -- 16. Goal-driven learning : fundamental issues and symposium report / by David B. Leake and Ashwin Ram -- 17. Storage side effects : studying processing to understand learning / by Lawrence W. Barsalou -- 18. Goal-driven learning in multistrategy reasoning and learning systems / by Ashwin Ram, Michael T. Cox and S. Narayanan -- 19. Inference to the best plan : a coherence theory of decision / by Paul Thagard and Elija Millgram -- 20. Toward goal-driven integration of explanation and action / by David B. Leake -- 21. Learning as goal-driven inference / by Ryszard Michalski and Ashwin Ram. English. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book. Computational learning theory. http://id.loc.gov/authorities/subjects/sh94004662 Théorie de l'apprentissage informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Doelgericht gedrag. gtt Leren. gtt Engineering & Applied Sciences. hilcc Computer Science. hilcc COMPUTER SCIENCE/Artificial Intelligence Ram, Ashwin. Leake, David B. has work: Goal-Driven Learning (Text) https://id.oclc.org/worldcat/entity/E39PCY4H44WhGfWxK9C3jmy9Kq https://id.oclc.org/worldcat/ontology/hasWork Print version: Goal-driven learning. Cambridge, Mass. : MIT Press, ©1995 0262181657 (DLC) 94049361 (OCoLC)31867498 Bradford book. FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1741 Volltext |
| spellingShingle | Goal-driven learning / Bradford book. Learning, goals, and learning goals / Planning to learn / Quantitative results concerning the utility of explanation-based learning / The use of explicit goals for knowledge to guide inference and learning / Deriving categories to achieve goals / Harpoons and long sticks : the interaction of theory and similarity in rule induction / Introspective reasoning using meta-explanations for multistrategy learning / Goal-directed learning : a decision-theoretic model for deciding what to learn next / Goal-based explanation evaluation / Planning to perceive / Planning and learning in PRODIGY : overview of an integrated architecture / A learning model for the selection of problem-solving strategies in continuous physical systems / Explicitly biased generalization / Three levels of goal orientation in learning / Characterizing the application of computer simulations in education : instructional criteria / Goal-driven learning : fundamental issues and symposium report / Storage side effects : studying processing to understand learning / Goal-driven learning in multistrategy reasoning and learning systems / Inference to the best plan : a coherence theory of decision / Toward goal-driven integration of explanation and action / Learning as goal-driven inference / Computational learning theory. http://id.loc.gov/authorities/subjects/sh94004662 Théorie de l'apprentissage informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Doelgericht gedrag. gtt Leren. gtt Engineering & Applied Sciences. hilcc Computer Science. hilcc |
| subject_GND | http://id.loc.gov/authorities/subjects/sh94004662 |
| title | Goal-driven learning / |
| title_alt | Learning, goals, and learning goals / Planning to learn / Quantitative results concerning the utility of explanation-based learning / The use of explicit goals for knowledge to guide inference and learning / Deriving categories to achieve goals / Harpoons and long sticks : the interaction of theory and similarity in rule induction / Introspective reasoning using meta-explanations for multistrategy learning / Goal-directed learning : a decision-theoretic model for deciding what to learn next / Goal-based explanation evaluation / Planning to perceive / Planning and learning in PRODIGY : overview of an integrated architecture / A learning model for the selection of problem-solving strategies in continuous physical systems / Explicitly biased generalization / Three levels of goal orientation in learning / Characterizing the application of computer simulations in education : instructional criteria / Goal-driven learning : fundamental issues and symposium report / Storage side effects : studying processing to understand learning / Goal-driven learning in multistrategy reasoning and learning systems / Inference to the best plan : a coherence theory of decision / Toward goal-driven integration of explanation and action / Learning as goal-driven inference / |
| title_auth | Goal-driven learning / |
| title_exact_search | Goal-driven learning / |
| title_full | Goal-driven learning / edited by Ashwin Ram and David B. Leake. |
| title_fullStr | Goal-driven learning / edited by Ashwin Ram and David B. Leake. |
| title_full_unstemmed | Goal-driven learning / edited by Ashwin Ram and David B. Leake. |
| title_short | Goal-driven learning / |
| title_sort | goal driven learning |
| topic | Computational learning theory. http://id.loc.gov/authorities/subjects/sh94004662 Théorie de l'apprentissage informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computational learning theory fast Doelgericht gedrag. gtt Leren. gtt Engineering & Applied Sciences. hilcc Computer Science. hilcc |
| topic_facet | Computational learning theory. Théorie de l'apprentissage informatique. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Computational learning theory Doelgericht gedrag. Leren. Engineering & Applied Sciences. Computer Science. |
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| work_keys_str_mv | AT ramashwin goaldrivenlearning AT leakedavidb goaldrivenlearning |