Machine Learning of Robot Assembly Plans:
The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch o...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1988
|
| Schriftenreihe: | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
51 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory |
| Beschreibung: | 1 Online-Ressource (XVIII, 234 p) |
| ISBN: | 9781461316916 |
| DOI: | 10.1007/978-1-4613-1691-6 |
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| 520 | |a The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory | ||
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Datensatz im Suchindex
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| author | Segre, Alberto Maria |
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| 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 | 10.1007/978-1-4613-1691-6 |
| format | Electronic eBook |
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| id | DE-604.BV045186791 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
| institution | BVB |
| isbn | 9781461316916 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030575968 |
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| physical | 1 Online-Ressource (XVIII, 234 p) |
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| publishDate | 1988 |
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| series2 | The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems |
| spelling | Segre, Alberto Maria Verfasser aut Machine Learning of Robot Assembly Plans by Alberto Maria Segre Boston, MA Springer US 1988 1 Online-Ressource (XVIII, 234 p) txt rdacontent c rdamedia cr rdacarrier The Kluwer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 51 The study of artificial intelligence (AI) is indeed a strange pursuit. Unlike most other disciplines, few AI researchers even agree on a mutually acceptable definition of their chosen field of study. Some see AI as a sub field of computer science, others see AI as a computationally oriented branch of psychology or linguistics, while still others see it as a bag of tricks to be applied to an entire spectrum of diverse domains. This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day. As one might imagine, however, this diversity also leads to genuine difficulties in assessing the significance and validity of AI research. These difficulties are an indication that AI has not yet matured as a science: it is still at the point where people are attempting to lay down (hopefully sound) foundations. Ritchie and Hanna [1] posit the following categorization as an aid in assessing the validity of an AI research endeavor: (1) The project could introduce, in outline, a novel (or partly novel) idea or set of ideas. (2) The project could elaborate the details of some approach. Starting with the kind of idea in (1), the research could criticize it or fill in further details (3) The project could be an AI experiment, where a theory as in (1) and (2) is applied to some domain. Such experiments are usually computer programs that implement a particular theory Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Industrieroboter (DE-588)4026861-5 gnd rswk-swf Lernfähigkeit (DE-588)4129684-9 gnd rswk-swf Industrieroboter (DE-588)4026861-5 s Lernfähigkeit (DE-588)4129684-9 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9781461289548 https://doi.org/10.1007/978-1-4613-1691-6 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Segre, Alberto Maria Machine Learning of Robot Assembly Plans Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Industrieroboter (DE-588)4026861-5 gnd Lernfähigkeit (DE-588)4129684-9 gnd |
| subject_GND | (DE-588)4026861-5 (DE-588)4129684-9 |
| title | Machine Learning of Robot Assembly Plans |
| title_auth | Machine Learning of Robot Assembly Plans |
| title_exact_search | Machine Learning of Robot Assembly Plans |
| title_full | Machine Learning of Robot Assembly Plans by Alberto Maria Segre |
| title_fullStr | Machine Learning of Robot Assembly Plans by Alberto Maria Segre |
| title_full_unstemmed | Machine Learning of Robot Assembly Plans by Alberto Maria Segre |
| title_short | Machine Learning of Robot Assembly Plans |
| title_sort | machine learning of robot assembly plans |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Industrieroboter (DE-588)4026861-5 gnd Lernfähigkeit (DE-588)4129684-9 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer science Artificial intelligence Industrieroboter Lernfähigkeit |
| url | https://doi.org/10.1007/978-1-4613-1691-6 |
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