The Self as an Embedded Agent | SpringerLink

The Self as an Embedded Agent CHRIS DOBBYN1 and SUSAN STUART2

1 Department of Computing, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK;

E-mail: [email protected]

2 Department of Philosophy, University of Glasgow, Glasgow, G12 8QQ, UK; E-mail:

[email protected]. uk Abstract. In this paper we consider the concept of a self-aware agent. In cognitive science agents are seen as embodied and interactively situated in worlds. We analyse the meanings attached to these terms in cognitive science and robotics, proposing a set of conditions for situatedness and embodiment, and examine the claim that internal representational schemas are largely unnecessary for intelligent behaviour in animats. We maintain that current situated and embodied animats cannot be ascribed even minimal self-awareness, and offer a six point definition of embeddedness, constituting minimal conditions for the evolution of a sense of self. This leads to further analysis of the nature of embodiment and situatedness, and a consideration of whether virtual animats in virtual worlds could count as situated and embodied. We propose that self-aware agents must possess complex structures of self-directed goals; multi-modal sensory systems and a rich repertoire of interactions with their worlds. Finally, we argue that embedded agents will possess or evolve local co-ordinate systems, or points of view, relative to their current positions in space and time, and have a capacity to develop an egocentric space. None of these capabilities are possible without powerful internal representational capacities. Key words: agent, embedded, embodied, representation, situated

1. Agents and Their Worlds 1.1. T HE PHILOSOPHICAL CONCEPT OF AN AGENT Philosophically the term ‘agent’ is reasonably uncomplicated, often being linked to other notions like self or subject. An agent is said to act autonomously rather than simply move, and whilst the movement of all living organisms could be explained in terms of simple, deterministic motion, the actions of agents, selves or subjects are generally thought not to be determined by a pre-set program or set of antecedent conditions. The agent behaves intentionally within its world, expressing a thought relation between itself as subject and some object, and, it is argued that from this “interplay between purposive action and changing environment” (Meijsing, 2000, p. 47) a sense of self evolves. Locke distinguishes two sorts of agent, the intellectual and the corporeal. The action of the former is expressed as ‘modes of thinking and willing’, whilst the action of the latter is ‘nothing else but modifications of matter’ (Locke, 1964, II, XXII, §11). This distinction is still familiar to us in the form of the action/movement dichotomy mentioned above, but any contemporary philosophical conception of an agent as a conscious subject of thought would combine the intellectual and Minds and Machines 13: 187–201, 2003. © 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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the corporeal agents to produce a single sensing, willing and acting system; an intentional system that engages and interacts with its world in a dynamic way; an agent that is the “controller [of its environment] and intentional producer of [its] thoughts” (Strawson, 1999, p. 108). An agent, then, in any rigorous sense of the word is conceived of as a mental thing, a thing with a ‘causal character’ that engages with its world in an interactive and manipulative way. It will be able to synthesise its internal representations, the world as it appears to it, and “be able to represent the order of [these] different appearances ... [from] its own point of view. ... [Thus a] minimal self-consciousness is a requirement on any kind of interpretation of experience.” (Blackburn, 1999, p. 138–139) This is not the definition of the term ‘agent’ we see expressed anywhere in cognitive science. 1.2. AGENTS IN COGNITIVE SCIENCE In cognitive science the terms agent, situated and embodied are now widely used, in varying definitions and interpretations. An agent may, for example, refer to any independent piece of software for some specific task, containing special algorithms or specifically encoded knowledge which will enable it to perform that task: examples of agents would thus be found in classical distributed systems, used to solve problems such as road traffic monitoring (Lesser and Corkhill, 1983) and air traffic control (Cammarata et al., 1983); in Artificial Life systems, where agents may evolve specialised behaviours (Aleksander, 1996) or display collective behaviour in concert with other agents (Mataric, 1992); and in independent robotic agents that can negotiate cluttered spaces and fulfil simple goals (Brooks, 1991a; Meyer and Wilson, 1991)—virtual or robotic agents of this kind are generally categorised under the umbrella term animats. Robotic and A-Life agents derive most of their interesting properties from being situated: they exist in a world, with that world directly influencing their behaviour, and which they, in turn, may influence. Agents can be embodied: in some way endowed with facilities to sense the world in which they are situated, to move through it and, in more limited ways, to manipulate it. But such ideas of agency, situatedness and embodiment are too all-inclusive and insufficiently precise to throw much light, at this stage, on questions of selfawareness. In the remainder of this section, we attempt to elucidate them further and to examine some of the difficulties that arise along the way. 1.3. AGENTS WITH GOALS If one were to interpret the term loosely enough, all software systems have goals: that is to say, end states which they are programmed to reach. Even the simplest systems, and the sub-units of such systems, have goals such as reading to the end of a file, terminating in a certain state, and so on; and with greater complexity

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goals become more layered and interdependent. Traditional Artificial Intelligence systems can often be characterised in terms of a search for a particular goal state, but this search may involve the establishment of numerous sub-goals and intermediate conclusions. For example, a chess playing program’s goal is to checkmate its opponent, but is programmed to achieve this by seeking to establish a succession of board positions in which it has minor advantages; expert systems prove intermediate conclusions on the way to establishing a diagnosis or some other result. In robotics, robots can be built to follow walls, pick up Coke cans, or perform simple cleaning tasks, these goals generally not being directly encoded into the robot but inherent in the pattern of connections between its sensory and actuating systems, as we discuss in later sections. However, they can still be viewed as goals—they can be thought of comprising the robot’s raison d’être. Most organisms above a certain level of complexity appear to have goals, even if they are simply the goals of survival and reproduction; and artificial organisms, such as computer viruses and boids (Reynolds, 1987), mimic these goals. Above all, human agents, as we know, have complex systems of goals, many of which conflict with one another. The concept of agency is inconceivable without the idea of a goal. 1.4. S ITUATED AGENTS For a natural or an artificial system to be justifiably termed an agent it cannot operate in isolation; it must be part of some larger system, performing some distinctive role in the larger system but still in some way be separable from it. By itself this is too liberal a definition: any module within a conventional data processing system could be said, under this account, to be an agent. But such a module only receives from its environment information of a very narrowly designated kind, across narrow, serial channels and produces only highly stereotypical output. Furthermore, the module can have no application, or continued existence, outside the software environment for which it was designed: even modules intended for reuse generally have to be tailored for their new environments (Meyer, 1996). So as a minimum an agent must be distinguishable from its environment yet coupled to it in such a way that it can respond to changes in it. In Distributed AI, Artificial Life and Robotics, agents derive much of their capability from being situated. It is hard to find any consensus on the meaning of this term, but Brooks (1991b, p. 575) defines it thus: ... (the robots) are situated in the world—they do not deal with abstract descriptions but with the here and now of the world directly influencing the behaviour of the system... The agents Brooks refers to here are robots that inhabit a three-dimensional, solid, ‘real’ world. Much of the thrust of Brooks’ criticism of earlier robotic endeavours such as Shakey (Nilsson, 1984) is that they were only able to function in extremely artificial environments, where lighting was carefully controlled, all objects were regular polyhedra and surfaces were painted to give maximum contrast. ‘Situated-

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ness’ for Brooks means the functioning of an agent within the three-dimensional world of objects that we inhabit. But, in claiming in Brooks (1991a) that an airline reservation system is an example of a situated system, accepting as it does messages coming into it unpredictably, Brooks seems to contradict his own later views. According to the criteria he sets, such a system could only very doubtfully be claimed to be situated in any sort of world: it senses nothing other than instances of the restricted class of electronic messages it can receive; admittedly it receives and processes these messages in real time, but it does not deal with “...the here and now of the (three-dimensional, solid, ‘real’) world”? Other theorists (Suchmann, 1987; Agre and Chapman, 1987) refer to situated actions, where decisions on which action to take are based on the agent’s position in the world and the state of the world at the moment the decision is to be taken. This view is echoed in Brooks’ insistence on the abandonment of absolute co-ordinate systems and the construction of adaptive distributed relational maps, in which agents identify and locate objects in relation to their position at any one time. A distinct, persistent and adaptive entity performing specialised tasks in real time, situated within a world or environment of some kind is possibly an adequate definition of agency in the sense that the term is commonly used in A-Life and Distributed AI; however, these are necessary but still insufficient conditions for an agent to which we could ascribe any level of self-awareness. 1.5. E MBODIED AGENTS A powerful strand of work in Cognitive Science deals with physically embodied agents. Such entities are situated in the real world of the offices and laboratories of their creators, are mobile and possess, as a minimum, sensory systems through which they can acquire information about their environment; they may also have actuators closely coupled to the sensory system, by means of which they can manipulate their environment. Aleksander (1996) also emphasises the complexity and closeness of this coupling between action, sensation and environment. But crucial here is what counts as a body in these physically embodied agents? Brooks cites an industrial paint-spraying robot as being embodied (though not situated, because it is not reactive). And are virtual bodies (in virtual worlds) to count as bodies— could Aleksander’s MAGNUS system, for example, be described as embodied? Although it does possess sensors and actuators, they are of a virtual kind only, and respond to a virtual world: MAGNUS exists only inside the memory of the workstation that is running it. For many roboticists, the answer must be no: not only do they regard the situation of animats in virtual worlds as a continuous temptation to cheat by building in convenient simplifications; they also argue that the physical world provides a semantic grounding for the representations with which a situated and embodied agent would deal, a ‘bottoming out’ of a potential representational regress (Smith, 1991). We return to the question of virtual bodies and virtual worlds in a later section.

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1.6. AGENTS WITH REPRESENTATIONS Representation has always been a key issue in traditional AI, with whole journals dedicated to the subject. AI systems such as planners and expert systems have universally had knowledge bases built in to them, containing symbolic representations of information required to carry out their tasks. Early robotic systems were thus programmed with some complete representation of the world they inhabited, which could be manipulated internally by means of algorithms that were also explicitly encoded. Other theoreticians have evolved the concept of deictic or indexical– functional representations (Agre, 1991), also sometimes termed internalised plans (Payton, 1990), in which world entities are represented entirely in terms of their relationship to the system at each particular moment in time, and which clearly relate to the situated actions discussed earlier. In the later strand of robotic research pioneered by Brooks it is claimed that robotic systems can be built that are nonrepresentational, in that they contain no inner abstract description of the world and no explicit encoding of their behaviour in algorithmic form (Brooks, 1991c). It is argued that when we observe an animat negotiating some territory, or disposing of empty Coke cans, it is we who ascribe high-level plans and intentions to it, as outside observers; but such plans do not exist inside the robot, which is simply reacting to its environment in a characteristic way. We do not believe that this argument can be sustained for animats of any complexity, still less for animats to which we might ascribe the existence of a self, however primitive; and in the penultimate section we argue strongly for the necessity of representation in any dynamic, and possibly self-aware, agent. 2. Embeddedness 2.1. T HE IDEA OF EMBEDDEDNESS The greatest mistake of traditional AI was to treat problems of sense, action and environment as irrelevant, or as side issues. And the growing realisation that these matters are, in fact, central—the move from a purely logical or computational to a more ecological or biological model of cognition—has meant that great strides have been made in our understanding of the nature and evolution of intelligent behaviour. However, despite these advances, the situated and embodied discussed so far could not be ascribed even the most minimal self-awareness because: • they are, in the main, reactive only: that is to say, their behaviour will only be in direct response to detected features of, or events in, their environment; • the goals of such systems are usually unitary, or else very restricted and stereotyped: an animat can be programmed to follow a wall or to retrieve and dispose of objects on the floor of a crowded office; but as yet a richer range of possible interactions has not been achieved; • they receive information from their situation across a simplified interface; although in recent work in robotics, an animat may receive information from

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more than one type of sensor, the range of possible input data to the system is generally quite sparse; • they have insufficient representational capacity. In the six points that follow we propose a definition of embeddedness, which we believe constitutes the minimal set of conditions for the possibility of self-awareness in an animat. The satisfaction of these conditions is necessary if the animat is to be termed an agent. Sometimes used simply as a synonym for situatedness, at other times taken to refer to the close coupling between a system’s intended behaviour and its environment (Aleksander, 1996), we believe along with other theorists (Haugeland, 1993) that embeddedness can be given a more radical interpretation. The following six conditions are, when taken together, necessary and sufficient for embeddedness: (1) the animat must be situated and embodied; (2) the animat must have multiple, self-directed goals; (3) the environment in which the animat is situated must be sufficiently complex and challenging for it to be capable of complex responses to it; (4) the animat must sense its world through a rich interface; (5) the animat must have a rich repertoire of possible interactions with its environment, continuously manipulating its world in ways that bring about significant changes. (6) the embedded animat must contain inner representations of itself and its world. We now examine some of the problems raised by this preliminary definition and attempt to show why self-awareness is unlikely to arise in any non-embedded system. 2.2. S ITUATED AND EMBODIED Stern (1985) has suggested that between the second and seventh month of life, the human child starts to form a distinction between itself and its world. An essential precondition of having a self must be the ability to make this distinction, and some grounds upon which to build it: no sense of agency or self-awareness is really conceivable without such a basic division. For an agent to have even the most reduced form of self-awareness it would have to be able to develop local co-ordinate systems, or points of view, locating, identifying and interacting with objects relative to its current spatial and temporal positions. This in turn implies the need for a body of some sort, part of an environment, but integrated and separable from it, and with perceivable limits that form a boundary or barrier between it and the wider world. Brewer (1992) contrasts the ‘egocentric space’ of the body with the ‘objective space’ of its situation, within which the self is able to locate itself. The notion of a body is problematic in itself, and we take up this issue in the ensuing sections. However, we believe the essential defining features of a body to be: a set of sensory channels through which information about the environment can be gleaned; an actuator system through which the world can be manipulated; and

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proprioceptive mechanisms that enable the system to evolve an inner ‘egocentric’ space, setting the limits between it and its world. These three channels not only form the boundary between egocentric space and objective space, but also that together they make an abstract definition of the term ‘body’. The commonsense notion we have of a body with boundaries implies that there is something external to that body. Thus any animat that is embodied must be situated in this minimal sense. However, a robot or some other entity sitting motionless in a dark cupboard is clearly not situated in any way that is likely to lead to the development of awareness, or of any complex internal structure at all. The situation of any embodied animat must exhibit complexity of some kind; and in what follows we examine this idea. 2.3. G OALS When we claim of an embedded animat that it must have multiple goals we are saying that it is necessary that it has, or is able to evolve, a more complex goal structure than that instantiated in simple software or in a ‘wall-hugging’ animat. The movements towards the attainment of the goal or goals in such systems can be explained in straightforward deterministic language, or by examining the physics that governs their internal and, possibly also, their external states. Any recourse to the discourse of rational autonomy or purposivity of action is pointless since it would be uninformative, and might even be taken as a signal of the misattribution of mental states. We have argued that an embedded animat is both situated and embodied, that such an animat inhabits and interacts with a complex environment and that, as a consequence, that animat will have a complex and changing manifold of internal representations. It is in this interplay between purposive action and changing environment that the animat needs to be able to recognise the goals that it is important for it to attain. Such goal recognition need not be intentional or self conscious— though we are proposing that any animat that is embedded in this strong sense will satisfy the requirements for the evolution or development of a minimal form of self-awareness; that it will be an agent in the senses of the term that we set out earlier—but it is necessary that the animat has this recognition if it is going to plan for their attainment; recognition and planning can, after all, mean the difference between survival and extinction. We argue further that the goals of this sort of agent are self-directed in the sense that they are conceived, and the means for their attainment executed, in the interest of the agent. In the language of cognitive science, their ultimate end is to bring about some internal state of the agent, rather than achieve some external state of the world. There are at least four specific internal states involved in this process, and many more as subsidiary states of the agent. Firstly, the agent is aware of its current internal state; let us say the agent is hungry. Secondly, it has a conception of an internal state that it would like to achieve, in our example the satiation of its

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hunger. Thirdly, it executes a plan to attain the goal, the top level of which will be, find and eat food. And, fourthly, the agent achieves its goal, the agent’s hunger is satisfied, and a new current internal state is formed. Such goals may increase the agents’ chances of survival—though in more complex agents the overall goal may be to increase the chances of a pleasurable survival rather than simply survival at all costs—and once pinpointed the agent is able to act purposively to bring about the ends it desires. Complex goals of this sort often manifest themselves as interdependent, or tangled, hierarchical goal structures, where the attainment of a final goal is only possible if an intermediate subgoal or set of subgoals is first satisfied. This action requires that an initial internal state—the final goal—be activated, and this activation sets in motion changes in other internal states which may in turn involve the execution or attainment of an external state(s) or subgoal(s), which in their own turn may cause changes in other internal states, and so the process continues until the final goal is attained. In any active agent there will be a fairly continuous comparison of internal state(s) with external state(s), and as subgoals are attained modifications will be made to the current state of the system and an assessment made of what is left to do. For example, it is only possible to do the crossword in the paper if I first buy a paper (or find a paper that has an uncompleted crossword), and it is only possible to buy a paper if I have sufficient financial resources and I go to the shop (or send someone for me), and it is only possible for me to go to the shop if I get out of bed, get dressed and open the door, and so on. My attainment of the final goal is beset by my having to attain a myriad of subgoals, and in its turn each subgoal is beset by my having to satisfy other conditions. Thankfully this process of final goal attainment is not endless. The crucial feature of about self-directed goals is that they are pursued in the interests of the agent, and it is this self-interest that distinguishes them from traditional AI systems like chess playing programs which also establish numerous subgoals and intermediate conclusions on their way to the attainment of their final goal. No sense of self, or even more simply of what is in the interest of the self, need be present in an AI system. In this context, the self that the agent might have a sense of, or may simply be interested in, is not necessarily a Cartesian ego, it may be “a kind of ‘selfless self’, in that it, in maintaining its own organization, distinguishes itself from what is not itself” (Meijsing, 2000, p. 47). If this is the case, then the animat could be self-aware in a truly minimal sense, yet able to evolve complex goal structures that operate to further its interests. 2.4. C OMPLEX WORLDS It does not seem likely that a robot moving across a flat plane, following a moving light, say, with no obstacles or surprises, would require inbuilt internal structures of any sophistication to perform well—such systems can be built using crossed wires; nor would it evolve any such intricacy in the course of adaptation to its

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environment. No planning, look-ahead, backtracking or stored patterns of experience would be required. So such a flat plane environment would be an example of a situation that is too simple for any animat inhabiting it to require the sort of internal representations that could lead to self-awareness. The situation of a self-aware animat must exhibit an acceptable degree of complexity in the sense of responding to and manipulating its environment to intentionally bring about change. But what does complexity mean in this context? Leaving aside technical definitions of the term taken from mathematics, an uncontroversial set of requirements for complexity can be drawn up simply by considering the situation of any organism or animat making a living in the physical world we inhabit. Our world is three-dimensional and irregular: it contains few regular shapes such as cubes or spheres—the natural world is resolutely fractal; it is constantly volatile, most changes not arising as a result of any actions taken by agents; these changes are thus surprising, in that most of them cannot practically be anticipated. The physical world is challenging, in the sense that at any time there may be countless states of affairs that potentially thwart the goals of the creatures that inhabit it; but it is also malleable: it is susceptible of change that results from actions that these creatures may take. Changes happen synchronously and in real time. Perhaps significantly, the physical world is an analogue world; that is, it can be characterised in terms of variables that vary continuously, as opposed to discretely (at the level of granularity at which organisms are able to perceive them). Altogether, the physical world can be characterised as a dynamic system, containing countless variables of significance to an animat, which are themselves coupled and which evolve over time. There can be no doubt that the situation of biological life among the challenges of the physical world has been a driving force in the evolution of complex living systems; and somewhere along the line of development a sense of self has arisen. But the question remains of whether an environment other than the physical world could lead to the development of a sense of self in artificial organisms. Do animats require physical bodies situated in the physical world? 2.5. V IRTUAL BODIES AND VIRTUAL WORLDS Would a software agent—a virtual organism, such as a computer virus, say, or Aleksander’s MAGNUS—receiving stimuli from a virtual world, count as being situated or embodied? Is the possession of a physical body, extended in 3-D space, an essential precondition for the development of self-awareness? It is hard to make an argument for the view that the physical world is especially privileged. We argued earlier that the boundaries of a body can be formed by three communication channels: sensory, actuating and proprioceptive. Now it is easy to construct a thought experiment in which a brain is disembodied in some nutrient bath and its afferent neural channels, sensory and proprioceptive, are given appropriate analogue stimuli, the

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process being controlled by a computer model of a 3-D world. More elaborately, impulses across the efferent actuating channel could be intercepted and fed into the computer model, feeding back into altered stimulation of the afferent channels to denote movement within, or change to, the world. To all intents and purposes, the unfortunate brain has a body—there is no way that the brain could tell that it had not—but this body is not extended in physical space, only in virtual space. If we accept that it is perfectly possible for a self-aware animat to have a body that has only a virtual existence and extension, we can make the same claim for the world such a body inhabits. As we argued above, there does not seem to be any good reason for believing that a three-dimensional world has any special properties that will guarantee the emergence of self-awareness; it is just as plausible to posit the idea of an animat inhabiting, and extended in, an eight-dimensional world: provided with appropriate sensory, proprioceptive and actuating capacities, the animat’s body would have virtual extension in such a higher-dimensional virtual space. And more esoteric situations are imaginable: picture an animat existing in a space of four exotic dimensions—say, Dolphin, Wireless, Bookbinder and Paint. Now, provided that a suitable metric can be defined on these dimensions, and thus distances between points in the DWBP space can be computed, there is no reason why an animat should not be embodied, locate itself and act within this virtual space. We have argued that a self-aware animat, an agent, must be able to locate itself, to have a point of view, relative to the space it inhabits: in principle, this is as possible within exotic spaces as it is within a physical, three-dimensional space. There are, of course, good pragmatic reasons for favouring the physical world as a situation for animats. We argued above that an animat’s situation is required to be complex; in principle, such complexity can be simulated, but in practice it is a very difficult endeavour, requiring a massive programming effort. The obvious advantage of the real world is that it is a given, and using it can avoid the huge simulation problems of mimicking the physical world, or creating a different sort of virtual world of sufficient complexity. The physical world gives you it all for free. 2.6. S ENSE , ACTION AND INTERACTION : A RICH INTERFACE We have argued that for an animat to be an agent it must be able to sense its world, bring about change in its world, and distinguish itself from its world; and for these abilities it will require sensing and actuating systems. The former enables an agent to acquire information about its environment, working as an outer sense, making it possible for the animat to determine its external state. The part of the system that senses the external world links, directly or indirectly, to actuators, making action, and hence interaction with the world, possible. But in more complex agents ‘sensing’ will comprise an ‘inner sense’ that not only enables the agent to determine its goal(s) and compare its sensory input with its current internal state(s), but also, if the agent is to make appropriate decisions about its action, to monitor

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its position in the world, its movement through the world and its actions within the world. Such an agent will maintain a ‘body schema’ that will provide it with “continually updated, non-conceptual, non-conscious information about [its] body ... [providing] the necessary feedback for the execution of... gross motor programs and their fine-tuning” (Meijsing, 2000, p. 39). An examination of case studies of people who have lost their ability to feel their body and thus create a body schema reveals, as Gibson (1979) thought, that the visual sense becomes important to their retaining a sense of self. In her relation of the case study of one patient, 1W, Meijsing says: “In the dark he did not know where his hand was; and even if he knew, he would not have been able to move it towards the bedside table without visual feedback” (Meijsing, 2000, p. 42). But, as she notes, this seems to be the replacement of an inner sense with an outer sense, and these may not equate to the same thing: the former would seem to be characterised by an immunity to error through misidentification (cf. Evans, 1982; Brewer, 1995) whilst the latter is not, and IW’s sense of self—or what might be taken to be the same thing, his idea of ‘not being dead’—returned only when he had learned to move again. There is, then, a strong case for saying that movement is crucial to the development of a sense of self, and that a sensory system is only necessary in organisms that move, because organisms that do not move have no capacity to avoid or manipulate objects. It is important to note that the opposite is also true: it is only with movement that a visual sense develops. (Held and Hem, 1963), and that vision itself can be characterised as a form of activity. It is not just the information of a changing environment, but the interplay between this information and the active self-movements that places the self firmly at the centre of the environment. Active self-movement gives a sense of agency, as the perceived environment changes as a result of the purposive action... (Meijsing, 2000, p. 46). Thus, if an animat is to become an agent with even a minimal sense of self it will have to be embodied and active, possessing a rich sensory interface through which it can detect its external states and represent them to itself from its own point of view, it must be able to compare those external states with its internal states in some dynamic way, and it must have a have a range of tools for movement and interaction with its world. 2.7. R EPRESENTATION As mentioned above, most recent work in robotics has focused on a tight coupling between perception and action, without any need for complex internal models of the world, or reasoning capacities operating over these models. And indeed such coupling mechanisms can be identified in many animals, including ourselves. A good example, to be found in humans and other primates, is the vestibulo-ocular reflex (Churchland and Sejnowski, 1992), an open-loop neural circuit that regulates the eyes’ tracking of a distant target as the head moves, and which survives in

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humans as a legacy from the predatory animals that are our evolutionary ancestors. A direct neural path exists between the semicircular canals in the ear that detect changes in the attitude of the head and the body and the motor neurons controlling the eye muscles: no ‘inner’ representational or cognitive system intervenes; the system operates by direct feedback between the sensory units (the semicircular canals) and the actuators (the motor neurons). Such reflex channels are common in animals with nervous systems at most levels of complexity; and perhaps it is this type of coupling that roboticists have in mind when they claim that the systems they are building are non-representational. Brooks, for instance, claims that Each activity producing layer connects perception to action directly. It is only the observer of the creature who imputes a central representation or central control. The Creature itself has none; it is a collection of competing behaviours... (Brooks, 1991c, p. 151). But such claims that an animat embodies a non-representational system should be examined carefully. As usual, there seems to be little consensus in the cognitive science community on any definition of representation, but many approaches associate the internal states of an animat and its behaviour (Wheeler, 1995). For an animat A, given any internal state of A, s(A), and any state in its environment, s(E), explanations of representation centre on the causal connection between the internal state s(A) and A’s behaviour, both in the presence and absence of s(E). All such definitions are open to counterexample, but this will serve as a working definition. Now, Brooks’ animats have been strictly engineered: the finite state machines that govern their low-level behaviour have been carefully contrived; and the patterns of connection and message passing between these machines are the result of much experiment; some of them evolve inner models of their world in the form of distributed maps (Mataric and Brooks, 1990). These animats have internal states. Indeed Brooks (1991b, p. 591) does not want to claim that his robots are non-representational systems; he makes only the weaker assertion that there is no central representation, no syntactic tokens that bear semantic weight, no variables and no rules; internal maps are temporary and relative to the animat’s current position. This may be readily granted. The internal states that the robots embody may be distributed and emergent, but they are still representations in the sense of being states (s(A) that stand for some state of the world (s(E)) or reaction to it in the form of A’s behaviour, and that have causal properties. Brooks labels such a view ‘extremist’, but it seems reasonable enough. A similar point might be made against dynamic systems theorists. The dynamic approach to AI conceptualises a situated system in terms of a vector of state variables, coupled to the environment, whose values change continuously, concurrently and interdependently over time. The internal behaviour of such a system may thus be characterised as a trajectory through some high-dimensional state space (van Gelder, 1998). But state vectors and state trajectories are not barred from being representational by not being symbolic; indeed there is a considerable body of work based on investigations of the representational capacities of dynamic and chaotic systems (Freeman, 1990;

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McKenna et al., 1994). Opponents of representational theories simply conceal a premise that asserts that the only possible form of representation is symbolic. It has also been argued that non-representationalists side-step crucial questions about cognition: by concentrating only on behaviour and the control issues that arise from it, at the expense of representation and concepts, whole areas of mental life are ruled out. Indeed it seems hardly credible that cognition of any but the most primitive sort would be possible without internal representations. Certainly no form of inner life could arise, because without any internal states standing in causal and denotational relations to world states, it would be impossible to treat with these world states at times when they were not immediately present to sense: hence, prescience, planning and imagination could not exist. No conceptual structures could be evolved, since such structures are not sensed directly; and without them higher functions such as language would also be impossible. Cognition is not a reflex like sneezing, nor is it really of a piece with learned but highly internalised behaviours such as walking, standing or catching, though it may have its roots in these; it is generally thought of as what is left after such behaviours are accounted for. But we claim that any more sophisticated forms of cognition, including selfawareness of any kind, are impossible without representation. Firstly, a self-aware agent will have to be able to establish a point of view, as we suggested above; minimally, then, the agent must have an internal representation of a point of view on its world that is relative and shifting in response to its movement within a changing world; this could arise from a distributed relational map, say, or some other form of representation. Secondly, a self-aware agent will have to be to synthesise and order its sensory impressions of the world from this point of view—it must evolve a notion of its impressions as part of a chain that persists over time, each new impression as being temporally related to the previous one. This need not imply that long-term memory is essential: short term feedback loops may account for the dawn of self-awareness in animals. But we have argued elsewhere (Stuart and Dobbyn, 2000) for the Kantian view that experience, and thus awareness, in natural or artificial animals, is impossible without a spatio-temporal framework of a certain sort: no possible sense of self could arise from a sequence of unrelated, momentary non-spatially-located experiences. And the cognitive apparatus that performs the unification of these experiences, whatever it is, must be characterisable in terms of internal states of the animat: it must be representational. Such representation of course need not be symbolic. Indeed, the internal processing of a self-aware animat would almost certainly be best described in terms of a dynamic system in which representations are state vectors and trajectories through state space, and it is possible that a dynamically embedded agent would utilise internal chaotic properties for representation and memory. But as causal and denotational internal states of the animat, they are representations and are indispensable.

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3. Conclusion In this paper we have attempted to articulate a conception of agency that attempts to accommodate the senses in which the term is used both in cognitive science and in philosophy. We have argued further that for even the most minimal awareness to exit in such an agent, the agent must be embedded, and we have proposed six criteria for embeddedness, which we believe are individually necessary, but in combination, sufficient conditions for agency that is characterised by a sense of self. Such an embedded agent can be viewed as a dynamic system that is intimately coupled to, and co-evolves over time with, a dynamic environment, the agent constantly sensing, representing and manipulating that environment. We acknowledge that any such radical conception of embeddedness must have its problems; for example, it is doubtful what use such a framework would be to the engineer of animats: vectors and vector trajectories in very high dimensional spaces are notoriously difficult to work with; and the embedded conception of mind and self-awareness is open to arguments about where the mind is actually situated—it is very hard, in such a picture, to draw a firm boundary between the mind and the world (Clark and Chalmers, 1998). Although they are beyond the scope of this paper, we believe that such objections can be met through the concept of inner representation and the nature of the interface between an animat’s internal processing and its world.

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