Extensions to Justification Theory
This work addresses foundational improvements in non-monotonic reasoning for AI and logic programming, but appears incremental as it builds on existing theory.
The paper proposes extensions to justification theory, a framework for semantics of non-monotonic logics, to enhance its applicability and utility in knowledge representation and computational implementations.
Justification theory is a unifying framework for semantics of non-monotonic logics. It is built on the notion of a justification, which intuitively is a graph that explains the truth value of certain facts in a structure. Knowledge representation languages covered by justification theory include logic programs, argumentation frameworks, inductive definitions, and nested inductive and coinductive definitions. In addition, justifications are also used for implementation purposes. They are used to compute unfounded sets in modern ASP solvers, can be used to check for relevance of atoms in complete search algorithms, and recent lazy grounding algorithms are built on top of them. In this extended abstract, we lay out possible extensions to justification theory.