Email this article Computational Mathematics in Medicine
Abstract
AI requires Logic. But its Classical version shows too many insufficiencies. So, it is very necessary to introduce more sophisticated tools, as may be 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, many times interconnect. Also it will be very useful, in the treatment of the problems of uncertainty and causality, the introduction of 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.
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, many times interconnect. Also it will be very useful, in the treatment of the problems of uncertainty and causality, the introduction of 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.
Keywords
Graph Theory, Bayesian Networks, Computational Biology, AI in Medicine