Much work in AI is fragmented, partly because the subject is so huge that it is difficult for anyone to think about all of it. Even within sub-fields, such as language, reasoning, and vision, there is fragmentation, as the sub-sub-fields are rich enough to keep people busy all their lives. However, there is a risk that results of isolated research will be unsuitable for future integration, e.g. in models of complete organisms, or human like robots. This paper offers a framework for thinking about the many components of visual systems and how they relate to the whole organism or machine. The viewpoint is biologically inspired, using conjectured evolutionary history as a guide to some of the features of the architecture. It may also be useful both for modelling animal vision and designing robots with similar capabilities. 1

Discusses some of the requirements for the control architecture of an intelligent human-like agent with multiple independent dynamically changing motives in a dynamically changing only partly predictable world. The architecture proposed includes a combination of reactive, deliberative and meta-management mechanisms along with one or more global "alarm" systems. The engineering design requirements are discussed in relation to our evolutionary history, evidence of brain function and recent theories of Damasio and others about the relationships between intelligence and emotions.

This paper contrasts the standard (in AI) ‘modular’ theory of the nature of vision with a more general theory of vision as involving multiple functions and multiple relationships with other sub-systems of an intelligent system. The modular theory (e.g. as expounded by Marr) treats vision as entirely, and permanently, concerned with the production of a limited range of descriptions of visible surfaces, for a central database; while the ‘labyrinthine’ design allows any output that a visual system can be trained to associate reliably with features of an optic array and allows forms of learning that set up new communication channels. The labyrinthine theory turns out to have much in common with J.J. Gibson's theory of affordances, while not eschewing information processing as he did. It also seems to fit better than the modular theory with neurophysiological evidence of rich interconnectivity within and between sub-systems in the brain. Some of the trade-offs between different designs are discussed in order to provide a unifying framework for future empirical investigations and engineering design studies. However, the paper is more about requirements than detailed designs.