Reasoning With Uncertain Knowledge
This addresses the challenge of handling uncertainty in knowledge representation for AI systems, though it appears incremental as it builds on existing symbolic support concepts.
The paper tackles the problem of representing uncertain knowledge by introducing a model where facts and their relationships are supported by other facts, enabling reasoning about both propositional knowledge and its underlying support structures.
A model of knowledge representation is described in which propositional facts and the relationships among them can be supported by other facts. The set of knowledge which can be supported is called the set of cognitive units, each having associated descriptions of their explicit and implicit support structures, summarizing belief and reliability of belief. This summary is precise enough to be useful in a computational model while remaining descriptive of the underlying symbolic support structure. When a fact supports another supportive relationship between facts we call this meta-support. This facilitates reasoning about both the propositional knowledge. and the support structures underlying it.