Their ubiquity suggests that they play an important role in intelligence. We conjecture that the cognitive value of partonomic knowledge lies in its ability to support translation between representations at different levels of granularity. This permits more effective problem solving by allowing reasoners to integrate the unique benefits that different representations can offer. We describe a representation for part-whole relations that supports this perspective. Decompositions identify the parts that compose objects.
Knowledge translations relate knowledge about objects to knowledge about their parts.
Our approach organizes partonomic knowledge around a taxonomy of decomposition functions. This allows us to use inheritance to share partonomic knowledge among similar decompositions. In recent years, researchers in artificial intelligence have become interested in replicating human physical reasoning talents in computers. One of the most important skills in this area is predicting how physical systems will behave. This thesis discusses an implemented program that generates algebraic descriptions of how systems of rigid bodies evolve over time.