Name
Abstract | ||
|---|---|---|
In this work we present a new method for qualitative simulation of dynamical systems. The method can be used to describe the possible time evolutions of different models, starting from a qualitative description of the main relationships among quantities. The status of each quantity in the model, at any instant, is synthesized using two main attributes, i.e. magnitude and rate.of.change; both can assume one of three possible qualitative values. The inference engine is based both on lisp programs and on different classes of production rules. Among others, the 'certainty.rules' have the task of recognizing all facts which can be asserted with certainty within the present context, whereas the 'hypothesis.rules' generate alternative contexts (or worlds) in which reasoning can continue independently of the others. Some examples of qualitative simulations obtained on simple models of the cardiovascular system are presented, and the main advantages and limitations of the method are discussed. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1016/0933-3657(92)90037-P | Artificial Intelligence In Medicine |
Keywords | Field | DocType |
different model,qualitative simulation,medical educational programs,main relationship,main attribute,physiological models,kee environment,main advantage,dynamic physiological model,different class,new method,possible time evolution,medical expert systems,qualitative description,possible qualitative value | Certainty,Qualitative simulation,Computer science,Lisp,Dynamical systems theory,Artificial intelligence,Inference engine,Machine learning,Qualitative reasoning | Journal |
Volume | Issue | ISSN |
4 | 1 | Artificial Intelligence In Medicine |
Citations | PageRank | References |
3 | 1.17 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ursino | 1 | 111 | 13.13 |
| G Avanzolini | 2 | 5 | 1.68 |
| P Barbini | 3 | 3 | 1.51 |