Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
This addresses the challenge of choosing among multiple plausible explanations in diagnosis, which is an incremental improvement for AI and diagnostic systems.
The paper tackles the multiple extension problem in diagnostic and default inference by proposing a framework that combines probabilities and defaults to compute probabilities incrementally while constructing explanations, using a branch and bound algorithm that finds the most probable explanation first when explanations are partially ordered.
The multiple extension problem arises frequently in diagnostic and default inference. That is, we can often use any of a number of sets of defaults or possible hypotheses to explain observations or make Predictions. In default inference, some extensions seem to be simply wrong and we use qualitative techniques to weed out the unwanted ones. In the area of diagnosis, however, the multiple explanations may all seem reasonable, however improbable. Choosing among them is a matter of quantitative preference. Quantitative preference works well in diagnosis when knowledge is modelled causally. Here we suggest a framework that combines probabilities and defaults in a single unified framework that retains the semantics of diagnosis as construction of explanations from a fixed set of possible hypotheses. We can then compute probabilities incrementally as we construct explanations. Here we describe a branch and bound algorithm that maintains a set of all partial explanations while exploring a most promising one first. A most probable explanation is found first if explanations are partially ordered.