Abstract
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 to the understanding of human operators’ skill by analysing operators’ traces using techniques of machine learning (ML). The paper gives a review of ML-based approaches to skill reconstruction. Recent work is presented with particular emphasis on understanding human tacit skill qualitatively, and automatically generating explanation of how it works. This includes qualitative machine learning to extract from operator’s control traces his subconscious sub-goals and control strategies described in qualitative terms.
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Bratko, I., Šuc, D. (2003). Understanding Control Strategies. In: Della Riccia, G., Dubois, D., Kruse, R., Lenz, HJ. (eds) Planning Based on Decision Theory. International Centre for Mechanical Sciences, vol 472. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2530-4_6
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DOI: https://doi.org/10.1007/978-3-7091-2530-4_6
Publisher Name: Springer, Vienna
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