Many people interact with a collection of man-made virtual machines (VMs) every day without reflecting on what that implies about options open to biological evolution, and the implications for relations between mind and body. This tutorial position paper introduces some of the roles different sorts of running VMs (e.g. single function VMs, “platform ” VMs) can play in engineering designs, including “vertical separation of concerns ” and suggests that biological evolution “discovered” problems that require VMs for their solution long before we did. This paper explains some of the unnoticed complexity involved in making artificial VMs possible, some of the implications for philosophical and cognitive theories about mind-brain supervenience and some options for design of cognitive architectures with self-monitoring and self-control.

Some AI researchers aim to make useful machines, including robots. Others aim to understand general principles of information-processing machines with various kinds of intelligence, whether natural or artificial, including humans and human-like systems. They primarily address scientific and philosophical questions rather than practical goals. However, the tasks required to pursue scientific and engineering goals overlap, since both involve building working systems to test ideas and demonstrate results, and the conceptual frameworks and development tools needed for both overlap. This paper, partly based on philosophical analysis of requirements for robots in complex 3-D environments, surveys varieties of meta-cognition, drawing attention to requirements that drove biological evolution and which are also relevant to ambitious engineering goals.

This chapter reports work done mostly by one member of the team – a philosopher with substantial AI programming experience, whose primary interests were in the very long term goals of the project, summarised in Chapter 1, including the goal of shedding light on problems solved by biological evolution,

Cognitive sensor networks are able to perceive, learn, reason and act by means of a distributed, sensor/actuator, computation and communication system. In animals, cognitive capabilities do not arise from a tabula rasa, but are due in large part to the intrinsic architecture (genetics) of the animal which has been evolved over a long period of time anddepends on a combination of constraints: e.g., ingest nutrients, avoid toxins, etc. We have previously shown how organism morphology arises from genetic algorithms responding to such constraints[6]. Recently, it has been suggested that abstract theories relevant to specific cognitive domains are likewise genetically coded in humans (e.g., language, physics of motion, logic, etc.); thus, these theories and models are abstracted from experience over time. We call this the Domain Theory Hypothesis, and other proponents include Chomsky [2] and Pinker [11] (universal language), Sloman [16, 17] (artificial intelligence), and Rosenberg [13] (cooperative behavior). Some advantages of such embedded theories are that they (1) make learning more efficient, (2) allow generalization across models, and (3) allow determination of true statements about the world beyond those available from direct experience. We have shown in previous work how theories of symmetry can dramatically improve representational efficiency and aid reinforcement learning on various problems [14]. However, it remains to be shown sensory data can be organized into appropriate elements so as to produce a model of a given theory. We address this here by showing how symmetric elements can be perceived by a sensor network and the role this plays in a cognitive system’s ability to discover knowledge about its own structure as well as about the surrounding physical world. Our view is that cognitive sensor networks which can learn these things will not need to be pre-programmed in detail for specific tasks. 1

This is an incomplete set of notes prepared for the workshop on Matching and Meaning, on 9th April 2009

What will it be like to admit Artificial Companions into our society? How will they change our relations with each other? How important will they be in the emotional and practical lives of their owners---since we know that people became emotionally dependent even on simple devices like the Tamagotchi? How much social life might they have in contacting each other? The contributors to this book assume that some form of long-term computer Companions are now a certainty in the coming years, and that it is a good moment to consider from a set of wide interdisciplinary perspectives, both how we shall construct them technically as well as their personal and social consequences. By Companions we mean conversationalists or confidants----not robots--- but rather computer software agents whose function will be to get to know their owners, who may well be elderly or lonely, and focusing not only on assistance via the internet (contacts, travel, doctors etc.) that many still find hard to use, but also on providing company and Companionship, by offering aspects of personalization. The human-Companion relationship could also be used to build a life narrative of the owner, eliciting over a long period a structure of the owner's life, perhaps in a level of detail that even their relatives might not recognize, or know about. You could call that autobiography