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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
16/02/1998 |
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Data da última atualização: |
12/12/2007 |
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Autoria: |
SHMEIL, M. A. H.; OLIVEIRA, E. |
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Título: |
Detecting the opportunities of learning from the interactions in a society of organizations. |
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Ano de publicação: |
1995 |
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Fonte/Imprenta: |
In: BRAZILIAN SYMPOSIUM ON ARTIFICIAL INTELLIGENCE, 12., 1995, Campinas. Advances in artificial intelligence: proceedings. Berlin: Springer, 1995. |
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Páginas: |
p.242-252 |
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Série: |
(Lecture Notes in Artificial Intelligence, 991; Lecture Notes in Computer Science). |
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ISBN: |
3-540-60436-7 |
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Idioma: |
Inglês |
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Notas: |
SBIA'95. Ed. by Jacques Wainer and Ariadne Carvalho. |
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Conteúdo: |
Organizations, as any complex and inherently distributed entities, are characterized by their internal and external interactions. Generally, and as a result of the continuous interactive process, the involved organizations become more efficient. This performance increase, achieved through resources optimization, can be seen as the outcome of a know-how acquired from previous interactions. In broad terms, the work presented in this paper can be classified as a contribution to the study and modeling of the behaviour of organizations. In particular, we are concerned of contracts between organization. This selection process can be characterized as an interative loop composed of an evaluation phase followed by a negotiation phase. During the selection activity, conflict resolution. Acording to the diverse selection methodologies that can be adopted, different learning opportunities can also be detected. The computational system under development, which supports the above mentioned interaction processes, is called ARTOR (ARTificial ORganizations), and is based o the Distributed Artificial Intelligence - Multi-agent Systems (DAI-MAS) and Symbolic Learning (SL) paradigms. Each ´component, or agent, is provided with the needed observation, planning, coordination, execution, communication and terning capabilities to perform its social role. |
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Palavras-Chave: |
Inteligenncia artificial. |
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Thesaurus Nal: |
artificial intelligence. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02153naa a2200205 a 4500
001 1005967
005 2007-12-12
008 1995 bl uuuu u00u1 u #d
020 $a3-540-60436-7
100 1 $aSHMEIL, M. A. H.
245 $aDetecting the opportunities of learning from the interactions in a society of organizations.
260 $c1995
300 $ap.242-252
490 $a(Lecture Notes in Artificial Intelligence, 991; Lecture Notes in Computer Science).
500 $aSBIA'95. Ed. by Jacques Wainer and Ariadne Carvalho.
520 $aOrganizations, as any complex and inherently distributed entities, are characterized by their internal and external interactions. Generally, and as a result of the continuous interactive process, the involved organizations become more efficient. This performance increase, achieved through resources optimization, can be seen as the outcome of a know-how acquired from previous interactions. In broad terms, the work presented in this paper can be classified as a contribution to the study and modeling of the behaviour of organizations. In particular, we are concerned of contracts between organization. This selection process can be characterized as an interative loop composed of an evaluation phase followed by a negotiation phase. During the selection activity, conflict resolution. Acording to the diverse selection methodologies that can be adopted, different learning opportunities can also be detected. The computational system under development, which supports the above mentioned interaction processes, is called ARTOR (ARTificial ORganizations), and is based o the Distributed Artificial Intelligence - Multi-agent Systems (DAI-MAS) and Symbolic Learning (SL) paradigms. Each ´component, or agent, is provided with the needed observation, planning, coordination, execution, communication and terning capabilities to perform its social role.
650 $aartificial intelligence
653 $aInteligenncia artificial
700 1 $aOLIVEIRA, E.
773 $tIn: BRAZILIAN SYMPOSIUM ON ARTIFICIAL INTELLIGENCE, 12., 1995, Campinas. Advances in artificial intelligence: proceedings. Berlin: Springer, 1995.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| 253. |  | MUZILLI, O.; OLIVEIRA, E. L.; TORNERO, M. T. Emprego da análise do solo e estimativa de doses econômicas para adubação fosfatada e potássica em cultura de milho no Estado do Paraná, Brasil. Pesquisa Agropecuária Brasileira, Brasília, v.17, n.2, p.203-209, fev. 1982. Título em inglês: Soil testing and estimation of economic rates of P and K fertilization for maize in Paraná State, Brazil.| Biblioteca(s): Embrapa Unidades Centrais. |
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