Using machine learning to understand operator's skill

Controlling complex,dynamic systems requires skills that operators often cannot completely describe, but can demonstrate. This paper describes research into the understanding of such tacit control skills. Understanding tacit skills has practical motivation in respect of communicating skill to other operators, operator training, and also mechanising and optimising human skill. This paper is concerned with approaches whereby, using techniques of machine learning, controllers that emulate the human operators are generated from examples of control traces. This process is also called "behavioural cloning". The paper gives a review of ML-based approaches to behavioural cloning, representative experiments, and an assessment of the results. Some recent work is presented with particular emphasis on understanding human tacit skill, and generating explanation of how it works. This includes the extraction of the operator's subconscious sub-goals and the use of qualitative control strategies. We argue for qualitative problem representations and decomposition of the machine learning problem involved.