This paper starts out from two observations: firstly, that there are complex links between what we term intelligence and what we term creativity and, secondly, that the phenomenon of surprise has a significant role in both the genesis and evaluation of creativity, and is tightly coupled to perception. We argue that for machines to develop to the point where we attribute to them intelligence and, therefore, their own degree of creativity, they must first develop a sensibility of surprise. This, we show, is predicated upon a multi-level organisation of perception, and a method of representing the interest, or novelty, of events and actions taking place in the physical world. A sensibility of surprise further depends on an ability to recognise the novelty of actions the system itself is contemplating. We describe methods of encoding surprise in perceptual robots, and show how this enables them to focus on what is interesting in their environment - a prerequisite to the production of behaviour both creative and intelligent.

We address the question of how artificial systems and natural organisms develop spatial competence. Most artificial systems draw upon considerable sophisticated operator- or developer-originated knowledge about what in the world sensor signals represent. Natural systems do not have such sophisticated auxiliary sources of information. We are interested in how, despite this, they achieve perceptual organisation, and suspect that the methods they use will have generalisable effectiveness. We describe a process that creates coherent mappings between the physical world and the phenomenological realm, analogous to retinotopicity and sensory homuncularity in natural systems, and discuss its application to problems of higher dimensionality and higher levels of abstraction. Importantly, such a process, having proved successful in the perceptual robotics domain of our current interests, is likely to be found in other cognitive domains because its strengths lie in its ability to organise and implicitly summarise data in the absence of clues about what that data represents.