Possible World Partition Sequences: A Unifying Framework for Uncertain Reasoning
This provides a foundational framework for integrating diverse uncertain reasoning systems, which is essential for building coherent AI systems that handle uncertainty from varied sources.
The paper tackles the problem of combining uncertain reasoning from multiple sources with different formalisms by introducing a unifying framework based on ordered partitions of possible worlds, called partition sequences, and demonstrates that it can incorporate existing formalisms like default logic and probabilistic conditioning.
When we work with information from multiple sources, the formalism each employs to handle uncertainty may not be uniform. In order to be able to combine these knowledge bases of different formats, we need to first establish a common basis for characterizing and evaluating the different formalisms, and provide a semantics for the combined mechanism. A common framework can provide an infrastructure for building an integrated system, and is essential if we are to understand its behavior. We present a unifying framework based on an ordered partition of possible worlds called partition sequences, which corresponds to our intuitive notion of biasing towards certain possible scenarios when we are uncertain of the actual situation. We show that some of the existing formalisms, namely, default logic, autoepistemic logic, probabilistic conditioning and thresholding (generalized conditioning), and possibility theory can be incorporated into this general framework.