Defeasible Reasoning via Datalog$^\neg$
This work addresses defeasible reasoning in computational logic, offering incremental improvements for specific logics and implementations.
The paper tackles the problem of compiling defeasible theories to Datalog$^\neg$ programs, proving correctness for the defeasible logic $DL(\\partial_{||})$ and identifying structural properties that enable efficient implementation and approximation compared to other logics.
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.