A Unified Framework for Nonmonotonic Reasoning with Vagueness and Uncertainty
This work addresses challenges in AI and logic for handling complex reasoning tasks with imprecision, though it appears incremental in extending existing methods.
The paper tackles the problem of nonmonotonic reasoning with vague and uncertain information by proposing an interval-valued fuzzy answer set programming paradigm, resulting in a system that can represent and reason with prioritized rules and exceptions, with an iterative method for answer set computation and identified termination conditions.
An interval-valued fuzzy answer set programming paradigm is proposed for nonmonotonic reasoning with vague and uncertain information. The set of sub-intervals of $[0,1]$ is considered as truth-space. The intervals are ordered using preorder-based truth and knowledge ordering. The preorder based ordering is an enhanced version of bilattice-based ordering. The system can represent and reason with prioritized rules, rules with exceptions. An iterative method for answer set computation is proposed. The sufficient conditions for termination of iterations are identified for a class of logic programs using the notion of difference equations.