Multi-level qualitative reasoning applied to CMOS digital circuits

Kaul, N.; Biswas, Gautam; Bhuva, B. L.

doi:10.1016/0954-1810(92)90001-i

Cited by 2 publications

(1 citation statement)

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“…Much work in qualitative simulation has been directed at the improvement and extension of the QSIM algorithm to deal with these problems, as illustrated by the examples in the previous sections. This has resulted in a qualitative simulation method capable of dealing with a number of non-trivial systems, like physiological processes (Ironi et al, 1990;Kuipers, 1989b;Ironi & Stefanelli, 1995;Rickel & Porter, 1997), (bio)chemical reactors (Molle & Edgar, 1990;Vinson & Ungar, 1995;Vatcheva, 2001) and chemical plants (Catino & Ungar, 1995), digital circuits (Kaul et al, 1992), fatigue and fracture in steel bridges (Roddis & Martin, 1992), and genetic and ecological networks (Heidtke & Schulze-Kremer, 1998;Guerrin & Dumas, 2001a, b). Moreover, QSIM and its relatives have been embedded in broader reasoning tasks, such as monitoring and diagnosis (Forbus, 1987;Oyeleye et al, 1990;DeCoste, 1991;Dvorak & Kuipers, 1991;Ng, 1991;Rose & Kramer, 1991;Lackinger & Nejdl, 1993;Vinson & Ungar, 1995;Dressler, 1996;Struss, 1997;Mosterman & Biswas, 1999;Panati & Dupré, 2000;Sachenbacher et al, 2000), model composition (Falkenhainer & Forbus, 1991;Low & Iwasaki, 1992;Farquhar, 1994;Nayak, 1995;Levy et al, 1997;Rickel & Porter, 1997), system identification (Söderman & Strömberg, 1991;Varšek, 1991;Richards et al, 1992;…”

Section: Discussionmentioning

confidence: 99%

Qualitative simulation and related approaches for the analysis of dynamic systems

Jong

2004

The Knowledge Engineering Review

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Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.

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