An Argumentative Approach for Explaining Preemption in Soft-Constraint Based Norms
This work addresses a specific problem in formal norm systems for researchers in AI and logic, offering an incremental improvement in explanation methods.
The paper tackles the challenge of understanding preemption in soft-constraint based norms, where higher-level norms override lower-level ones, by proposing a derivation state argumentation framework (DSA-framework) to explain how preemption arises from evolving situational knowledge, and formally proves that under local optimality, it can provide explanations for obligatory or forbidden consequences.
Although various aspects of soft-constraint based norms have been explored, it is still challenging to understand preemption. Preemption is a situation where higher-level norms override lower-level norms when new information emerges. To address this, we propose a derivation state argumentation framework (DSA-framework). DSA-framework incorporates derivation states to explain how preemption arises based on evolving situational knowledge. Based on DSA-framework, we present an argumentative approach for explaining preemption. We formally prove that, under local optimality, DSA-framework can provide explanations why one consequence is obligatory or forbidden by soft-constraint based norms represented as logical constraint hierarchies.