owner: gmh .heading('the problem with robots is...'); designing them by hand from scratch is difficult for humans, while designing them automatically (e.g. using learning or evolution) doesn't scale up to complicated tasks. .heading('shaping'); is a way of integrating high-level domain knowledge supplied by humans, with low-level learning supplied by the computer.

The basic principles of shaping are:

The idea is that shaping methods are analogous to a high-level language for describing robot tasks - the designer specifies the task in a high-level declarative fashion, and the learning algorithm compiles this into a procedural implementation. By insulating the high-level design from the low-level implementation, the designer is forced to think about the task at a more appropriate level, and the learning algorithms are freed from the constraint of having to produce humanly understandable solutions. .heading('architectures'); Shaping requres the use of an underlying learning architecture with some unusual features, specifically the ability to learn sub-behaviours and then incorporate them into higher level behaviours without explicit directions from a human designer. To satisfy this and other requirements we have developed a neural implementation of a classifier system. .heading('publications'); Shaping is discussed in more detail in the paper:

Robot Shaping - Principles, Methods and Architectures (Gzip'd Postscript), by Simon Perkins and Gillian Hayes, presented at the Workshop onLearning in Robots and Animals, at AISB'96, University of Sussex, UK, April 1-2, 1996.