A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents

TL;DR

This paper provides a comprehensive review of knowledge acquisition methods across human, human-inspired, and autonomous agents, highlighting their evolution and proposing a new classification framework.

Contribution

It introduces a novel classification of knowledge acquisition methods into three categories and discusses the emergence of a fourth, co-evolutionary approach.

Findings

Knowledge acquisition methods are classified into human, human-inspired, and autonomous categories.

The field is increasingly active due to rapid changes in human activity systems.

A new fourth category based on red-teaming and co-evolution is discussed.

Abstract

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.