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
A dictionary based on concept coherence
Artificial Intelligence
Artificial Intelligence
Casual reconstruction: understanding casual descriptions of physical systems
Casual reconstruction: understanding casual descriptions of physical systems
The teachable language comprehender: a simulation program and theory of language
Communications of the ACM
Transition Space
Reasoning about hidden mechanisms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Understanding behavior using consolidation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
This paper introduces the causal reconstruction task the task of reading a causal description of a physical system, forming an internal model of the specified behavior, and answering questions demonstrating comprehension and reasoning on the basis of the input description. A representation called transition space is introduced, in which events are depicted as path fragments in a space of "transitions," or complexes of changes in the attributes of participating objects. By identifying partial matches between the transition space representations of events, a program called PATHFINDER is able to perform causal reconstruction on short causal descriptions presented in simplified English. Simple transformations applied to event representations prior to matching enable the program to bridge discontinuities arising from the writer's use of analogy or abstraction. The operation of PATHFINDER is illustrated in the context of a simple causal description extracted from the Encyclopedia Americana, involving exposure of film in a camera.