Towards a general framework for program generation in creative domains

Choosing an efficient artificial intelligence approach for producing artefacts for a particular creative domain can be a difficult task. Seemingly minor changes to the solution representation and learning parameters can have an unpredictably large impact on the success of the process. A standard approach is to try various different setups in order to investigate their effects and refine the technique over time. Our aim is to produce a pluggable framework for exploring different representations and learning techniques for creative artefact generation. Here we describe our initial work towards this goal, including how problems are specified to our system in a format that is concise but still able to cover a wide range of domains. We also tackle the general problem of constrained solution generation by bringing information from the constraints into the generation and variation process and we discuss some of the advantages and disadvantages of doing this. Finally, we present initial results of applying our system to the domain of algorithmic art generation, where we have used the framework to code up and test three different representations for producing artwork.