Defeasible Reasoning via Datalog$^\neg$
Michael J. Maher
TL;DR
The paper presents a metaprogramming-based method to compile the scalable defeasible logic ${ DL}(\partial_{||})$ into $Datalog^\neg$ under the well-founded semantics $WF91$, with a proof of correctness and a linear-size translation. By transforming a propositional Metaprogram of the defeasible theory into a stratified, rule-aware form, it enables sound approximations and efficient execution even on incomplete $Datalog^\neg$ engines. The work identifies structural properties of ${ DL}(\partial_{||})$ that support efficient implementation, innovation in partial evaluation and unfolding/folding transformations, and a detailed pathway from a defeasible theory to a practical, scalable logic-programming representation. The approach is generalizable to other defeasible logics and provides a blueprint for leveraging metaprograms to bridge nonmonotonic reasoning with mature logic-programming ecosystems, offering both exact and approximate execution strategies. These contributions advance scalable reasoning in defeasible logics and inform design choices for future defeasible systems and their implementations.
Abstract
We address the problem of compiling defeasible theories to Datalog$^\neg$ programs. We prove the correctness of this compilation, for the defeasible logic $DL(\partial_{||})$, but the techniques we use apply to many other defeasible logics. Structural properties of $DL(\partial_{||})$ are identified that support efficient implementation and/or approximation of the conclusions of defeasible theories in the logic, compared with other defeasible logics. We also use previously well-studied structural properties of logic programs to adapt to incomplete Datalog$^\neg$ implementations.
