ML92 Proceedings of the ninth international workshop on Machine learning
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Building symbolic representations of intuitive real-time skills from performance data
Machine intelligence 13
Machine Learning - special issue on inductive logic programming
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Problem Decomposition for Behavioural Cloning
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Inducing Models of human Control Skills
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Framework for Behavioural Cloning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Control Skill, Machine Learning and Hand-crafting in Controller Design
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Skill reconstruction as induction of LQ controllers with subgoals
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Skill modeling through symbolic reconstruction of operator's trajectories
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
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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.