A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Qualitative analysis of MOS circuits
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Causal and Teleological Reasoning In Circuit Recognition
Causal and Teleological Reasoning In Circuit Recognition
Critical hypersurfaces and the quantity space
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Abstraction by time-scale in qualitative simulation
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Critical hypersurfaces and the quantity space
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Abstraction by time-scale in qualitative simulation
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
QPC: a compiler from physical models into qualitative differential equations
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A qualitative agent-based approach to power quality monitoring and diagnosis
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
A model-based approach to the diagnosis of the cardiac arrhythmias
Artificial Intelligence in Medicine
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Qualitative simulation is a key inference process in qualitative causal reasoning, In this paper, we present the QSIM algorithm, a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches. QSIM is an efficient constraint-satisfaction algorithm that can follow either its standard semantics allowing the creation of new landmarks, or the {+, 0, -} semantics where 0 is the only landmark value, by changing a table of legal state-transitions. We argue that the QSIM semantics make more appropriate qualitative distinctions since the {+, 0, -} semantics can collapse the distinction among increasing, stable, or decreasing oscillation. We also show that (a) qualitative simulation algorithms can be proved to produce every actual behavior of the mechanism being modeled, but (b) existing qualitative simulation algorithms, because of their local points of view, can predict spurious behaviors not produced by any mechanism satisfying the structural description. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.