{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pct-and-beyond-towards-a-computational","title":"PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems","arxiv_id":"1611.05379","date":"2016-11-16","proceeding":null,"authors":["Prof. Roger K. Moore"],"abstract":"Recent years have witnessed increasing interest in the potential benefits of\n`intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot,\niRobot's Roomba robot vacuum cleaner and Google's driverless cars have fired\nthe imagination of the general public, and social media buzz with speculation\nabout a utopian world of helpful robot assistants or the coming robot\napocalypse! However, there is a long way to go before autonomous systems reach\nthe level of capabilities required for even the simplest of tasks involving\nhuman-robot interaction - especially if it involves communicative behaviour\nsuch as speech and language. Of course the field of Artificial Intelligence\n(AI) has made great strides in these areas, and has moved on from abstract\nhigh-level rule-based paradigms to embodied architectures whose operations are\ngrounded in real physical environments. What is still missing, however, is an\noverarching theory of intelligent communicative behaviour that informs\nsystem-level design decisions in order to provide a more coherent approach to\nsystem integration. This chapter introduces the beginnings of such a framework\ninspired by the principles of Perceptual Control Theory (PCT). In particular,\nit is observed that PCT has hitherto tended to view perceptual processes as a\nrelatively straightforward series of transformations from sensation to\nperception, and has overlooked the potential of powerful generative model-based\nsolutions that have emerged in practical fields such as visual or auditory\nscene analysis. Starting from first principles, a sequence of arguments is\npresented which not only shows how these ideas might be integrated into PCT,\nbut which also extend PCT towards a remarkably symmetric architecture for a\nneeds-driven communicative agent. It is concluded that, if behaviour is the\ncontrol of perception, then perception is the simulation of behaviour.","url_abs":"http://arxiv.org/abs/1611.05379v1","url_pdf":"http://arxiv.org/pdf/1611.05379v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[{"method_slug":"pct","method_name":"PCT"}],"datasets_introduced":[],"methods_introduced":[{"slug":"pct","name":"PCT","full_name":"Perceptual control theoretic architecture"}],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}