Unifying time and uncertainty for diagnosis
Eugene Santos · Journal of Experimental & Theoretical Artificial Intelligence · 1996
Existing models of diagnosis have not unified the notions of time, uncertainty and abduction which are necessary in domains such as clinical diagnosis, dynamic systems modelling, and fault diagnosis. We present a new approach which models information through linear constraints and find that many aspects of our knowledge-base and inferencing mechanism are naturally suited towards these constraints. Furthermore, this approach properly subsumes existing abductive and temporal models and provides a precise framework for explanatory reasoning.