A Model for Non-Monotonic Reasoning Using Dempster's Rule
This addresses a foundational issue in AI reasoning for researchers, offering an incremental improvement over prior methods.
The paper tackles the problem of non-monotonic reasoning in belief function frameworks, where Dempster's rule produces errors, by proposing a new interpretation that combines rules more compatibly with probabilistic results and modifies the normalization factor to yield more intuitive outcomes.
Considerable attention has been given to the problem of non-monotonic reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed solutions introducing meta-rules which recognized conditional independencies in a probabilistic sense. More recently an e-calculus formulation of default reasoning (J. Pearl) shows that the application of Dempster's rule to a non-monotonic situation produces erroneous results. This paper presents a new belief function interpretation of the problem which combines the rules in a way which is more compatible with probabilistic results and respects conditions of independence necessary for the application of Dempster's combination rule. A new general framework for combining conflicting evidence is also proposed in which the normalization factor becomes modified. This produces more intuitively acceptable results.