| Título: | Computational Methods in Medicine |
| Autores: | Angel Garrido; National University of Distance Education, Madrid |
| Fecha: | 2010-01-10 |
| Publicador: | BRAIN: Broad Research in Artificial Intelligence and Neuroscience |
| Fuente: |
 |
| Tipo: | Peer-reviewed Article |
| Tema: |
Mathematics, Computational Biology, Artificial Intelligence |
| Descripción: | Artificial Intelligence requires Logic. But its Classical version shows too many insufficiencies. So, it is absolutely necessary to introduce more sophisticated tools, such as Fuzzy Logic, Modal Logic, Non-Monotonic Logic, and so on [2]. Among the things that AI needs to represent are Categories, Objects, Properties, Relations between objects, Situations, States, Time, Events, Causes and effects, Knowledge about knowledge, and so on. The problems in AI can be classified in two general types[3, 4], Search Problems and Representation Problem. There exist different ways to reach this objective. So, we have [3] Logics, Rules, Frames, Associative Nets, Scripts and so on, that are often interconnected. Also, it will be very useful, in dealing with problems of uncertainty and causality, to introduce Bayesian Networks and particularly, a principal tool as the Essential Graph. We attempt here to show the scope of application of such versatile methods, currently fundamental in Medicine. |
| Idioma: | Inglés |
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