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