Portfolio optimization as a learning platform for control education and research
ACC'09 Proceedings of the 2009 conference on American Control Conference
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The development of assignments for undergraduate teaching typically requires a compromise between what is achievable by an average student and what will engage the interest of a more advanced member of the class. Selecting a suitable compromise is particularly problematic for undergraduate Artificial Intelligence (AI) courses which typically attempt to cover a very broad range of topics, without delving too deeply into the details. Ideally, a single problem would be selected whose solution could be approached with more than one technique covered in the course, enabling students to carry out a comparative analysis of performance. Robot soccer simulation has provided an interesting platform for Artificial Intelligence research and is increasingly being used as a teaching apparatus. There are a number of limitations with existing simulation methodologies for this purpose. Current robot soccer simulators are aimed at research groups where accuracy is paramount and all facets of the real system must be emulated. However, many of the intricacies of a real robot soccer player are inappropriate for a teaching environment, as they detract from desired learning outcomes. Consequently, there is a need for a simulation that employs a simplified set of game rules and dynamics. This paper describes the design and implementation of such a framework and presents experiences gained from its use as a third year practical.