Analysis of models and metacognitive architectures in intelligent systems
Recently Intelligent Systems (IS) have highly increased the autonomy of their decisions, this has been achieved by improving metacognitive skills. The term metacognition in Artifi cial Intelligence (AI) refers to the capability of IS to monitor and control their own learning processes. This paper de...
- Autores:
-
Caro Piñeres, Manuel Fernando
Jiménez Builes, Jovani Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2013
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/73150
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/73150
http://bdigital.unal.edu.co/37625/
- Palabra clave:
- Artifi cial Intelligence
Metacognition
Metamemory
MetaComprehension
SelfRegulation
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Recently Intelligent Systems (IS) have highly increased the autonomy of their decisions, this has been achieved by improving metacognitive skills. The term metacognition in Artifi cial Intelligence (AI) refers to the capability of IS to monitor and control their own learning processes. This paper describes different models used to address the implementation of metacognition in IS. Then, we present a comparative analysis among the different models of metacognition. As well as, a discussion about the following categories of analysis: types of metacognition architectural support of metacognition components, architectural cores and computational implementations. |
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