Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition
What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were bas...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1990
|
| Schriftenreihe: | Machine Learning
92 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base |
| Beschreibung: | 1 Online-Ressource (IV, 152 p) |
| ISBN: | 9781461315315 |
| DOI: | 10.1007/978-1-4613-1531-5 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV045186368 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180912s1990 |||| o||u| ||||||eng d | ||
| 020 | |a 9781461315315 |9 978-1-4613-1531-5 | ||
| 024 | 7 | |a 10.1007/978-1-4613-1531-5 |2 doi | |
| 035 | |a (ZDB-2-ENG)978-1-4613-1531-5 | ||
| 035 | |a (OCoLC)1053815833 | ||
| 035 | |a (DE-599)BVBBV045186368 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-634 | ||
| 082 | 0 | |a 006.3 |2 23 | |
| 245 | 1 | 0 | |a Knowledge Acquisition: Selected Research and Commentary |b A Special Issue of Machine Learning on Knowledge Acquisition |c edited by Sandra Marcus |
| 264 | 1 | |a Boston, MA |b Springer US |c 1990 | |
| 300 | |a 1 Online-Ressource (IV, 152 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Machine Learning |v 92 | |
| 520 | |a What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base | ||
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Artificial intelligence | |
| 700 | 1 | |a Marcus, Sandra |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781461288213 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4613-1531-5 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-ENG | ||
| 940 | 1 | |q ZDB-2-ENG_Archiv | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030575545 | ||
| 966 | e | |u https://doi.org/10.1007/978-1-4613-1531-5 |l BTU01 |p ZDB-2-ENG |q ZDB-2-ENG_Archiv |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804178877277274112 |
|---|---|
| any_adam_object | |
| author2 | Marcus, Sandra |
| author2_role | edt |
| author2_variant | s m sm |
| author_facet | Marcus, Sandra |
| building | Verbundindex |
| bvnumber | BV045186368 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-1-4613-1531-5 (OCoLC)1053815833 (DE-599)BVBBV045186368 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4613-1531-5 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02965nmm a2200421zcb4500</leader><controlfield tag="001">BV045186368</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180912s1990 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461315315</subfield><subfield code="9">978-1-4613-1531-5</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4613-1531-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-ENG)978-1-4613-1531-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1053815833</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045186368</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Knowledge Acquisition: Selected Research and Commentary</subfield><subfield code="b">A Special Issue of Machine Learning on Knowledge Acquisition</subfield><subfield code="c">edited by Sandra Marcus</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, MA</subfield><subfield code="b">Springer US</subfield><subfield code="c">1990</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (IV, 152 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Machine Learning</subfield><subfield code="v">92</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Marcus, Sandra</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781461288213</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4613-1531-5</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-ENG_Archiv</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030575545</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-1-4613-1531-5</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_Archiv</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV045186368 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:57Z |
| institution | BVB |
| isbn | 9781461315315 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030575545 |
| oclc_num | 1053815833 |
| open_access_boolean | |
| owner | DE-634 |
| owner_facet | DE-634 |
| physical | 1 Online-Ressource (IV, 152 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_Archiv ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 1990 |
| publishDateSearch | 1990 |
| publishDateSort | 1990 |
| publisher | Springer US |
| record_format | marc |
| series2 | Machine Learning |
| spelling | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition edited by Sandra Marcus Boston, MA Springer US 1990 1 Online-Ressource (IV, 152 p) txt rdacontent c rdamedia cr rdacarrier Machine Learning 92 What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Marcus, Sandra edt Erscheint auch als Druck-Ausgabe 9781461288213 https://doi.org/10.1007/978-1-4613-1531-5 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence |
| title | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition |
| title_auth | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition |
| title_exact_search | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition |
| title_full | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition edited by Sandra Marcus |
| title_fullStr | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition edited by Sandra Marcus |
| title_full_unstemmed | Knowledge Acquisition: Selected Research and Commentary A Special Issue of Machine Learning on Knowledge Acquisition edited by Sandra Marcus |
| title_short | Knowledge Acquisition: Selected Research and Commentary |
| title_sort | knowledge acquisition selected research and commentary a special issue of machine learning on knowledge acquisition |
| title_sub | A Special Issue of Machine Learning on Knowledge Acquisition |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence |
| url | https://doi.org/10.1007/978-1-4613-1531-5 |
| work_keys_str_mv | AT marcussandra knowledgeacquisitionselectedresearchandcommentaryaspecialissueofmachinelearningonknowledgeacquisition |