Approximating Defeasible Logics to Improve Scalability
This work addresses scalability problems for researchers and practitioners in AI and legal informatics, but it is incremental as it builds on existing logics.
The paper tackles the scalability issue in defeasible logics, which are used in legal document representation and explainable AI, by proposing to use the more scalable $DL(\\partial_{||})$ as a computational aid for $DL(\\partial)$ and other logics, identifying conditions for substitution and partial use without changing conclusions.
Defeasible rules are used in providing computable representations of legal documents and, more recently, have been suggested as a basis for explainable AI. Such applications draw attention to the scalability of implementations. The defeasible logic $DL(\partial_{||})$ was introduced as a more scalable alternative to $DL(\partial)$, which is better known. In this paper we consider the use of (implementations of) $DL(\partial_{||})$ as a computational aid to computing conclusions in $DL(\partial)$ and other defeasible logics, rather than as an alternative to $DL(\partial)$. We identify conditions under which $DL(\partial_{||})$ can be substituted for $DL(\partial)$ with no change to the conclusions drawn, and conditions under which $DL(\partial_{||})$ can be used to draw some valid conclusions, leaving the remainder to be drawn by $DL(\partial)$.