Abstract
Belief base revision has been studied within the answer set programming framework. We go a step further by introducing uncertainty and studying belief base revision when beliefs are represented by possibilistic logic programs under possibilistic answer set semantics and revised by certain input. The paper proposes two approaches of rule-based revision operators and presents their semantic characterization in terms of possibilistic distribution. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results.
Keywords
- Knowledge Representation and Reasoning: Non-monotonic Reasoning
- Knowledge Representation and Reasoning: Belief Change
- Uncertainty in AI: Uncertainty Representations
- Knowledge Representation and Reasoning: Reasoning about Knowlege and Belief
