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Recent years have witnessed increasing interest in the potential benefits of`intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot,iRobot's Roomba robot vacuum cleaner and Google's driverless cars have firedthe imagination of the general public, and social media buzz with speculationabout a utopian world of helpful robot assistants or the coming robotapocalypse! However, there is a long way to go before autonomous systems reachthe level of capabilities required for even the simplest of tasks involvinghuman-robot interaction - especially if it involves communicative behavioursuch as speech and language. Of course the field of Artificial Intelligence(AI) has made great strides in these areas, and has moved on from abstracthigh-level rule-based paradigms to embodied architectures whose operations aregrounded in real physical environments. What is still missing, however, is anoverarching theory of intelligent communicative behaviour that informssystem-level design decisions in order to provide a more coherent approach tosystem integration. This chapter introduces the beginnings of such a frameworkinspired by the principles of Perceptual Control Theory (PCT). In particular,it is observed that PCT has hitherto tended to view perceptual processes as arelatively straightforward series of transformations from sensation toperception, and has overlooked the potential of powerful generative model-basedsolutions that have emerged in practical fields such as visual or auditoryscene analysis. Starting from first principles, a sequence of arguments ispresented which not only shows how these ideas might be integrated into PCT,but which also extend PCT towards a remarkably symmetric architecture for aneeds-driven communicative agent. It is concluded that, if behaviour is thecontrol of perception, then perception is the simulation of behaviour.

PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems