Incremental Probabilistic Inference
This addresses the need for incremental probabilistic inference in problem solvers, but it appears incremental in nature.
The paper tackles the problem of probabilistic inference systems lacking incremental support by proposing a system based on a smaller grain-size inference task, achieving the desired incrementality for low-level probabilistic representation services.
Propositional representation services such as truth maintenance systems offer powerful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incrementality typically demanded by problem solvers. The problem, we argue, is that the basic task of probabilistic inference is typically formulated at too large a grain-size. We show how a system built around a smaller grain-size inference task can have the desired incrementality and serve as the basis for a low-level (propositional) probabilistic representation service.