Non-monotonic Reasoning and the Reversibility of Belief Change
This addresses a conceptual limitation in belief revision for AI and logic, but it is incremental as it builds on prior work on RPMs.
The paper tackled the problem of iterated belief change in non-monotonic reasoning, showing that ranked preferential models (RPMs) fail to allow for reversibility of belief change, indicating a need for numerical belief strengths.
Traditional approaches to non-monotonic reasoning fail to satisfy a number of plausible axioms for belief revision and suffer from conceptual difficulties as well. Recent work on ranked preferential models (RPMs) promises to overcome some of these difficulties. Here we show that RPMs are not adequate to handle iterated belief change. Specifically, we show that RPMs do not always allow for the reversibility of belief change. This result indicates the need for numerical strengths of belief.