A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
SYMSAC '71 Proceedings of the second ACM symposium on Symbolic and algebraic manipulation
Representation of Models for Expert Problem Solving in Physics
IEEE Transactions on Knowledge and Data Engineering
Dynamic domain abstraction through meta-diagnosis
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Shifting ontological perspectives in reasoning about physical systems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Behavioral aggregation within complex situations: a case study involving dynamic equilibria
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Automatically analyzing a steadily beating ventricle's iterative behavior over time
Artificial Intelligence in Medicine
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Solving design and analysis problems in physical worlds requires the representation of large amounts of knowledge. Recently, there has been much interest in explicitly making assumptions to decompose this knowledge into smaller Models. A crucial aspect of problem-solving paradigms based on models is that they include methods to automatically, and efficiently, select and change models. We represent physical domains as Graphs of Models, where models are the nodes of the graph and the edges are the assumptions that have to be changed in going from one model to the other. This paper describes the methods used in the Graphs of Models paradigm for changing models. This knowledge can be represented qualitatively, permitting fast inference mechanisms that provide powerful model changing behaviors.