Non-deterministic Distance Semantics for Handling Incomplete and Inconsistent Data

We introduce a modular framework for formalizing reasoning with incomplete and inconsistent information. This framework is composed of non-deterministic semantic structures and distance-based considerations. The combination of these two principles leads to a variety of entailment relations that can be used for reasoning about non-deterministic phenomena and are inconsistency-tolerant. We investigate the basic properties of these entailments and demonstrate their usefulness in the context of model-based diagnostic systems.

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