Combining Multiple-Valued Logics in Modular Expert Systems

TL;DR

This paper addresses the challenge of enabling communication between modules in large expert systems that use different truth-functional multiple-valued logics for managing uncertainty, ensuring inference preservation.

Contribution

It analyzes the communication problem between modules with different uncertainty calculi, focusing on truth-functional multiple-valued logics and inference-preserving communication.

Findings

Communication can be achieved while preserving inference across different logics.

The analysis provides a framework for integrating modules with diverse uncertainty calculi.

The approach facilitates modularity in complex expert systems.

Abstract

The way experts manage uncertainty usually changes depending on the task they are performing. This fact has lead us to consider the problem of communicating modules (task implementations) in a large and structured knowledge based system when modules have different uncertainty calculi. In this paper, the analysis of the communication problem is made assuming that (i) each uncertainty calculus is an inference mechanism defining an entailment relation, and therefore the communication is considered to be inference-preserving, and (ii) we restrict ourselves to the case which the different uncertainty calculi are given by a class of truth functional Multiple-valued Logics.