Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Reasoning about assumptions in graphs of models
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
The origin and resolution of ambiguities in causal arguments
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Taming intractible branching in qualitative simulation
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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The analysis of large complex situations poses difficult problems for qualitative reasoning due to the complexity of reasoning from first principles and the proliferation of ambiguities. Abstraction is a promising Bolution to these problems. In this paper, we study a type of abstraction, behavioral aggregation--the process of grouping a set of individual entities that collectively behave as a unit. In particular, we show how to build aggregate models of situations involving dynamic equilibria and how to reason about their behavior. Finally, we demonstrate, through several examples, the benefits of reasoning at the aggregate level: a reduction in the complexity of reasoning and a compact, easily interpretable, description of the behavior.