Ordinal Conditional Functions for Nearly Counterfactual Revision
This addresses a theoretical problem in belief revision for AI and logic, but appears incremental as it builds on existing Ordinal Conditional Functions.
The paper tackles belief revision for conditional statements with nearly false antecedents by using Ordinal Conditional Functions with infinite values, proposing a model where only specific hypothetical implausibility levels are revised through simple arithmetical operations.
We are interested in belief revision involving conditional statements where the antecedent is almost certainly false. To represent such problems, we use Ordinal Conditional Functions that may take infinite values. We model belief change in this context through simple arithmetical operations that allow us to capture the intuition that certain antecedents can not be validated by any number of observations. We frame our approach as a form of finite belief improvement, and we propose a model of conditional belief revision in which only the "right" hypothetical levels of implausibility are revised.