Behavioural cloning is a simple and effective technique for automatically and non-intrusively producing comprehensible and implementable models of human control skill. Behavioural cloning applies machine learning techniques to behavioural trace data, in a transparent manner, and has been very successful in a wide range of domains. The limitations of early behavioural cloning work are: that the clones lack goal-structure, are not robust to variation, are sensitive to the nature of the training data and often produce complicated models of the control skill. Recent behavioural cloning work has sought to address these limitations by adopting goal-structured task decompositions and combining control engineering representations with more sophisticated machine learning algorithms. These approaches have had some success but by compromising either transparency or robustness. This thesis addresses these limitations by investigating: new behavioural cloning representations, control structures, data processing techniques, machine learning algorithms, and performance estimation and testing techniques. First a novel hierarchical decomposition of control is developed, where goal settings and the control skill to achieve them are learnt. This decomposition allows feedback control mechanisms to be combined with modular goal-achievement. Data processing limitations are addressed by developing data-driven, correlative and sampling techniques, that also inform the development of the learning algorithm. The behavioural cloning process is developed by performing experiments on simulated aircraft piloting tasks, and then the generality of the process is tested by performing experiments on simulated gantry-crane control tasks. The performance of the behavioural cloning process was compared to existing techniques, and demonstrated a marked improvement over these methods. The system is capable of handling novel goal-settings and task structure, under high noise conditions. The ability to produce successful controllers was greatly improved by using the developed control representation, data processing and learning techniques. The models produced are compact but tend to abstract the originating control behaviour. In conclusion, the control representation and cloning process address current limitations of behavioural cloning, and produce reliable, reusable and readable clones.
| Identifer | oai:union.ndltd.org:ADTP/272503 |
| Date | January 2009 |
| Creators | Isaac, Andrew Paul, Computer Science & Engineering, Faculty of Engineering, UNSW |
| Publisher | Awarded By:University of New South Wales. Computer Science & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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