Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Smadar Kedar"'
Publikováno v:
Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society ISBN: 9781315789354
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bdcbd13466936dca54dfb85185c48bb3
https://doi.org/10.4324/9781315789354-6
https://doi.org/10.4324/9781315789354-6
Publikováno v:
Knowledge Acquisition. 6:179-196
This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd1cf77fe4cee7d73e0e2e450a8fe2f1
https://doi.org/10.1006/knac.1994.1010
https://doi.org/10.1006/knac.1994.1010
Autor:
Subbarao Kambhampati, Smadar Kedar
Publikováno v:
Artificial Intelligence. 67:29-70
Most previous work in explanation-based generalization (EBG) of plans dealt with totally ordered plans. These methods cannot be directly applied to generalizing partially ordered partially instantiated plans, a class of plans that have received signi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ae1a1d11204ca808ad12700f4983a0c
https://doi.org/10.1016/0004-3702(94)90011-6
https://doi.org/10.1016/0004-3702(94)90011-6
Publikováno v:
ACM SIGART Bulletin. 2:61-65
This paper describes the Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, tempo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b46b77b43f71159785b588b7ea973afb
https://doi.org/10.1145/122344.122355
https://doi.org/10.1145/122344.122355
Publikováno v:
CHI Conference Companion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cce53c696b4391e150272d837dc88926
https://doi.org/10.1145/257089.257197
https://doi.org/10.1145/257089.257197
Autor:
Benjamin Bell, Smadar Kedar
Publikováno v:
CHI 95 Conference Companion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e908c8cc8260d0e3406528881d13143
https://doi.org/10.1145/223355.223454
https://doi.org/10.1145/223355.223454
Publikováno v:
Proceedings of the second ACM international conference on Multimedia - MULTIMEDIA '94.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e98ad39fe79a315ddd4163751efdae9
https://doi.org/10.1145/192593.192650
https://doi.org/10.1145/192593.192650
Publikováno v:
INTERCHI Adjunct Proceedings
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab1fada2040226064917f72bef1a6b46
https://doi.org/10.1145/259964.260182
https://doi.org/10.1145/259964.260182
Publisher Summary Most research on machine learning and planning has involved performance systems based on classical problem-solving algorithms (for example, STRIPS-Iike planners). AI problem solving has taken various divergent roads from these class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91e176a42ef9e515d32eead81861f957
https://doi.org/10.1016/b978-1-4832-0774-2.50011-1
https://doi.org/10.1016/b978-1-4832-0774-2.50011-1
Autor:
Kathleen B. McKusick, Smadar Kedar
Planning systems which make use of domain theories can produce more accurate plans and achieve more goals as the quality of their domain knowledge improves. MTR, a multi-strategy learning system, was designed to learn from system failures and improve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c9bfcbba93319b8ea6f202b6610bbc5
https://doi.org/10.1016/b978-0-08-049944-4.50043-4
https://doi.org/10.1016/b978-0-08-049944-4.50043-4