A unified setting for inference and decision: An argumentation-based approach

TL;DR

This paper introduces a unified argumentation framework that integrates inference from inconsistency and decision making, accommodating various decision types and handling incoherent information in AI systems.

Contribution

It presents a novel general framework that combines inference and decision processes, extending existing argumentation models to handle uncertainty, multiple criteria, and incoherent data.

Findings

Framework unifies inference and decision making in AI.

Handles incoherent information without requiring coherence.

Applicable to diverse decision types like rule-based and case-based.

Abstract

Inferring from inconsistency and making decisions are two problems which have always been treated separately by researchers in Artificial Intelligence. Consequently, different models have been proposed for each category. Different argumentation systems [2, 7, 10, 11] have been developed for handling inconsistency in knowledge bases. Recently, other argumentation systems [3, 4, 8] have been defined for making decisions under uncertainty. The aim of this paper is to present a general argumentation framework in which both inferring from inconsistency and decision making are captured. The proposed framework can be used for decision under uncertainty, multiple criteria decision, rule-based decision and finally case-based decision. Moreover, works on classical decision suppose that the information about environment is coherent, and this no longer required by this general framework.