Abstract: "Temporal ambiguity is an intrinsic problem of qualitative reasoning in predicting complex systems, because many quantized variables in a system may change simultaneously and qualitative reasoning therefore cannot determine the correct temporal order of transitions in the system. Conv...

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Bibliographische Detailangaben

Format:	Buch
Sprache:	English
Veröffentlicht:	Tokyo, Japan 1991
Schriftenreihe:	Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 697
Schlagworte:	Künstliche Intelligenz Artificial intelligence Reasoning
Zusammenfassung:	Abstract: "Temporal ambiguity is an intrinsic problem of qualitative reasoning in predicting complex systems, because many quantized variables in a system may change simultaneously and qualitative reasoning therefore cannot determine the correct temporal order of transitions in the system. Conventional methods for temporal disambiguation require total model representation, which provides global system information, but constrains the reasoning. A method is introduced which uses a total model, but reasoning is done with local information, represented by variables and local relations among variables This new reasoning method reduces the temporal ambiguity using local information without global information, where the variables are given hierarchical structure according to a time-scale of transitions. Variables within each hierarchy are treated separately, so faster and slower transitions are predicted independently. Predicting the faster transitions is given priority. After each slower transition prediction, predication [sic] is shifted back to the faster transitions. Therefore, the prediction is more accurate than with conventional methods. As a result, this method can deal with cyclic, [sic] behavior in addition to monotonic behavior.
Beschreibung:	15 S.