Grounding Rule-Based Argumentation Using Datalog

TL;DR

This paper introduces a novel grounding method for rule-based argumentation in ASPIC+ by translating first-order rules into Datalog, enabling scalable reasoning while maintaining correctness.

Contribution

It proposes an intelligent grounding procedure that translates ASPIC+ instances into Datalog and simplifies grounding, addressing exponential growth issues in rule-based argumentation.

Findings

The method effectively reduces grounding size.

Empirical results demonstrate scalability.

Correctness of reasoning is preserved.

Abstract

ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.