Automating Defeasible Reasoning in Law
This addresses the challenge of handling complex legal norms and contracts for AI systems, but it appears incremental as it builds on existing rule-based and logic-based methods.
The paper tackles the problem of automating defeasible reasoning in legal contexts by identifying rule modifiers and defining transformations to translate rules into formulas, comparing classical logic with SMT solvers and non-monotonic logic with ASP solvers for reasoning.
The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifiers, leading in the end to a translation of rules to formulas. For reasoning with and about rules, we contrast two approaches, one in a classical logic with SMT solvers as proof engines, one in a non-monotonic logic with Answer Set Programming solvers.