Many different methods for combining human expertise with machine learning in general, and evolutionary computation in particular, are possible. Which of these methods work best, and do they outperform human design and machine design alone? In order to answer this question, a human-subject experiment for comparing human-assisted machine learning methods was conducted. Three different approaches, i.e. advice, shaping, and demonstration, were employed to assist a powerful machine learning technique (neuroevolution) on a collection of agent training tasks, and contrasted with both a completely manual approach (scripting) and a completely hands-off one (neuroevolution alone). The results show that, (1) human-assisted evolution outperforms a manual scripting approach, (2) unassisted evolution performs consistently well across domains, and (3) different methods of assisting neuroevolution outperform unassisted evolution on different tasks. If done right, human-assisted neuroevolution can therefore be a powerful technique for constructing intelligent agents.

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In 2011 IJCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT), July 2011.

@inproceedings{karpov:ijcaiws11, title={Assisting Machine Learning Through Shaping, Advice and Examples}, author={Igor Karpov and Vinod Valsalam and Risto Miikkulainen}, booktitle={2011 IJCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT)}, month={July}, url="http://nn.cs.utexas.edu/?karpov:ijcaiws11", year={2011} }