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Communication and Cognition - Artificial Intelligence vol. 17 no. 3-4, pp. 133-157, 2000.

Autonomy and the emergence of intelligence: Organised interactive construction

W.D. Christensen and C.A. Hooker1

October 1999 I: Interactive constructivism II: An interactivist-constructivist theory of autonomy III: Autonomy as the foundational concept for an I-C theory of embodied cognition IV: Conclusion

Abstract

This paper outlines an interactivist-constructivist theory of autonomy as

the basic organisational form of life, and the role we see it playing in a theory of embodied cognition. We distinguish our concept of autonomy from autopoiesis, which does not emphasise interaction and openness. We then present the basic conceptual framework of the I-C approach to intelligence, including an account of directed processes, dynamical anticipation, normative evaluation, and self-directedness as the basis of intelligence and learning, and use this to briefly reflect on other contemporary dynamical systems approaches. I: Interactive constructivism

This paper presents a theory of autonomy as the basic organisational form

of life, and an interactivist-constructivist paradigm for modelling intelligence which takes autonomy as its central concept. Our work is broadly concerned with developing a naturalistic theory of intelligent agency as an embodied feature of organised, typically living, dynamical systems. Agents are entities which engage in normatively constrained, goal-directed, interaction with their environment. Intelligent agents have goals appropriate to their situation and interact with the environment in ways which adaptively achieve those goals. Humans are paradigm intelligent agents, and understanding agency is an important component of our self-understanding as individuals-within-communities and as a species. However, the culturally received basis of our self-understanding – our ‘folk psychology’ – rests uneasily with recent perspectives on human nature sourced from scientific disciplines such as evolutionary biology and neurobiology. Furthermore our

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understanding of intelligence and agency is insufficiently refined to provide clear principles for extending the concepts to other natural and artificial entities such as non-human animals and robots. Some of these systems display elements common to human intelligence, like context-sensitive adaptive behaviour, information processing of various kinds, and even sociality, however they generally lack other aspects of human intelligence such as a science and language. It is currently far from clear what intelligence and agency-related concepts are appropriate for describing the various kinds of non-human adaptive systems, and whether and how one might draw a boundary marking agents off from other kinds of systems.

What is required is an integrative theory capable of synthesising the

various research programs involved in studying intelligent agents within a common framework of the kind which characterised classical philosophical models of rationality and artificial intelligence. The term ‘autonomous agent’ has gained considerable prominence in recent artificial intelligence work on robotics and computer programing as those fields have come to increasingly emphasise the importance of adaptive independent behaviour (e.g. Beer 1990, Maes 1990). However comparatively little work has been done to defend the application of the concept in these contexts or to develop an explicit theory of agency (see Smithers 1995), and there remains much controversy concerning the appropriate theoretical language and models for describing the systems in question (e.g. Brooks 1991, Beer 1995, Clark 1997). Likewise, there is controversy over the appropriate models for understanding the adaptive behaviour of animals ranging in complexity from ants and bees to higher primates.

Our approach is start from the ground up — to look for the foundations of

agency in the basic characteristics of living systems, and to understand the development of intelligent agents in terms of the elaboration and specialisation of these basic capacities. We do so from an interactivist-constructivist (I-C) perspective, which is a form of naturalism based on a process metaphysics. I-C assumes that the higher order properties associated with life and mind, including norms, functions and meaning, are constructed through complex interaction processes. I-C has natural affinities with developmentalist approaches in biology, dynamical and situated approaches to cognitive science, and embodied constructivist approaches to meaning and representation.

We model the basic organisation of life with a theory of autonomous

systems — self-structuring far-from-equilibrium systems which seek out energy gradients that can maintain their dissipative processes and also act to maintain and sometimes modify and elaborate the processes which enable the exploitation

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of such gradients. In this picture intelligence is characterised as a capacity for context-sensitive action, and the emergence of intelligence as a distinctive adaptive strategy is associated with a form of adaptability focussed on complex action in variable environments. The constraints associated with context-sensitive adaptive action provide the basis for a unifying common framework for functional and epistemic norms. Moreover, since intelligence must develop through constructive learning processes which build on the success and failure of action, understanding the complex normative constraints associated with adaptive action provides the basis for understanding the dynamics of these processes. We briefly explore the interactive and evolutionary nature of constructive learning processes, developing a model of self-directed anticipative learning as the constructive process associated with strong forms of cognitive development.

II: Natural organisation: A theory of autonomy

The concept of autonomy is designed to capture the general organisational

nature of living systems.2 If intelligence is firmly rooted in natural life capacities, as was suggested above, then a foundational theory of this type is necessary in order to properly understand the nature and emergence of intelligent systems.

Living systems are a particular kind of cohesive system, where a cohesive

system is one in which there are dynamical bonds amongst the elements of the system which individuate the system from its environment (see Collier 1988, Christensen, Collier and Hooker 1999). These bonds fall into different organisational kinds — some cohesive systems are based on stable structural relationships which cause the components of the system to bind together statically (e.g. rocks), whilst others are based on process relationships which continuously re-create the system (e.g. cells). The latter are what is here called autonomous systems, systems whose integrity arises from self-generating, self-reinforcing processes.

To gain an intuitive feel for the systemic distinctions being made compare

a gas, a rock, and a living cell. A gas has no internal cohesion, it takes whatever shape and condition its containing environment imposes and will simply disperse if not externally constrained. By contrast, a rock possesses internal bonds which constrain the behaviour of its elements in such a way that the rock behaves dynamically as an integral whole. The notable organisational features of these cohesive bonds are passivity, rigidity and localisation. The bonds are passive and rigid in that they are stable deep energy well interactions which constrain the constituent molecules to spatial positions within a crystal lattice. The bonds are

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localised in the sense that the strength of the forces which bind a molecule within the crystal lattice depend only on the connections with adjacent molecules. This localisation means that there are no essential constraints on where the boundaries of the rock must occur — if it is split the particularity of the rock’s identity is disrupted, but the result is two smaller rocks with exactly the same type of cohesion properties as the original.

A living cell is similar to a rock inasmuch as it possesses cohesive bonds

which cause it to behave as an integrated whole, however organisationally it is very different to the rock. In particular, the cohesion of a cell is active, flexible and holistic. The chemical bonds of a cell are formed by shallow energy well interactions; they have short time scales relative to the life of the cell and must be continually actively remade with the assistance of external energy fluxes. This continuous activity makes the cell vulnerable to disruption but also gives the cell flexibility since the interactions can vary according to circumstances by responding sensitively to system and environmental changes. The cohesion of a cell is holistic because the forces which bind its parts depend on globally organised interactions. That is, local interactions must form functional processes that interact at the global level of the cell to reproduce the conditions necessary for the cell’s survival. As a result of this holistic organisation, cutting a cell in two usually does not produce two new cells (in contrast with the rock) because the processes which regenerate the cell are disrupted.

Autonomous systems are cohesive systems whose organisation is of the

same general type as the cell. That is, autonomous systems are actively self-generating systems constituted in complex processes that are sustained by open cycles of interaction, internally and with the environment. This means that the cohesion conditions of autonomous systems: (1) tend to rely on relatively shallow energy wells, (2) are nonstationary, with the dynamical conditions underpinning cohesion being characteristically oscillatory or chaotic in nature, (3) rely essentially on self-generated dynamical conditions, and (4) achieve dynamical self-generation through the possession of an internal organisation of interactions which perform work to direct energy fluxes from the environment into these same cohesion-generating processes. Condition 3 identifies autonomous systems as being types of positive feedback systems, typically with stabilising negative feedback as well. Condition 4 specifies that the generation of organisation in autonomous systems is substantially internal (see further below). Condition 4 distinguishes genuinely autonomous systems from other kinds of phase-separated positive feedback systems; in principle from systems, such as candle flames, which exercise no self-regenerative regulation (whence the determination of system features lie outside of whatever organisation there is), and in degree from

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systems, such as populations of viral parasites, where relatively little (but not no) regulation of regeneration resides within the system. We refer to the organisation of interactions satisfying conditions 3 and 4 as the system’s directive organisation.3

To get a sense of the diversity of autonomous systems consider the

following examples: • �Molecular catalytic bi-cycles: Mutually catalytic molecules which form a

self-sustaining bi-cycle system (Rebek Jr. 1994). This is the minimal case of a self-generated process-based system. The directive organisation which generates the process-patterning lies in the macro-molecular conformation which catalyses the formation of the mirror template molecule from the substrate material.

• �Organisms: The paradigm examples of autonomous systems are uni- and multi- cellular organisms, where cell and skin membranes differentiate internal and external environments; metabolic systems maintain critical physiological parameters for system functioning (pH level, temperature, stored energy in forms such as ATP); and, for the more deeply complex multi-cellular organisms, an immune system destroys harmful invaders while a sensori-motor/cognitive system regulates environmental interaction, seeking out critical resources (food, water, shelter, mate) and avoiding danger (poisons, predators etc.).

• �Species: The autonomy of a species lies in the way the population is regenerated over time through evolutionary adaptation. Mutation and ontogeny (the feedforward aspects of evolution) allow a species to explore its genomic and phenotypic configurational possibilities. Natural selection (the feedback aspect of evolution) eliminates unfit phenotypes (and their corresponding genomes). The net effect is that the species forms an autonomous process regenerated by a feedback cycle between the population and the environment as fit genomic-ontogenetic configurations proliferate and the species as a whole explores its organisational possibilities, evolving to its accessible, environmentally successful organisational forms. Together, genetic and ontogenetic exploration and negative feedback result in at least the maintenance of a stable macro cycle.4

• �Colonies: Human cities, e.g., actively import the resources from around them essential to maintaining and elaborating their functions (water, foods, fuels, materials, information ...) and distribute these through complex transport

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pathways so as to preserve their functional integrity, they have many processes which respond to deficiencies in supply and other internal threats to coherence, from private procurement in markets and charity work to public regulation of procurement (e.g. for water), internal restitution (e.g. educational production) and regulation (e.g. policing, cf. immune function). It is this complex form of organisation which accounts for the fact that cities are simultaneously both very resilient in some respects and highly fragile in others.

Thus, although all autonomous systems share characteristic features, there is also considerable organisational variety.

Autonomous systems are cohesive self-generating systems, but it would be

a mistake to interpret the term ‘autonomy’ as implying complete independence from the environment. Indeed, there are at least three clear ways in which autonomous systems are not independent of their environment: (1) as dynamically open systems, autonomous systems are coupled to their environments by nonlinear interactions and hence cannot be analytically decomposed as a linear sum of system plus environment, (2) as far-from-equilibrium dissipative systems, autonomous systems require energy input from the environment, (3) as adaptive systems, the functional organisation of autonomous systems must be characterised in relation to at least some of the determinable features of the environment, indeed to the extent that they have organisational depth because they rely on environmental order, their depth must be characterised in terms of that order.

So, rather than involving complete independence from the environment,

autonomy as it is being theorised here involves a certain kind of organisational asymmetry between the system and environment, namely that the constraints shaping energy flows from the environmental milieu into system-constitutive processes is substantially endogenous to the system itself. Although aspects of the environment participate in the overall process-cycle in and by which the system is constituted, they require the system’s directed processes to become channeled into system-distinctive processes. For example, a particular bird species may depend on the presence of twigs to make nests, but it does not depend on any particular twigs since the birds can choose from what is available and twigs of themselves have no tendency to play a role in bird-creating processes unless co-opted by birds. So whilst autonomous systems depend on dynamical and organisational features of the environment (twigs, for example, are organised), they are distinctively characterised by internal directive organisation and consequent pattern creation capacity upon which their existence

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depends. Not all dissipative processes constitute autonomous systems in the sense

outlined above because a substantial part of the directive organisation critical to their existence lies outside the system. Many process-based systems, such as Bénard cells, are wholly determined by the presence of external energy fluxes; these systems are driven by their environment. Other systems contain some of the directive organisation necessary for their existence endogenously, however this directive organisation is incomplete and they rely essentially on external sources of organisation to structure the processes essential for their cohesion. Viruses, for example, rely on the genetic machinery of the host cell to reproduce. Directively incomplete systems must rely on relations with other systems to achieve self-generation.

Autonomous systems are interactively self-generating: they so interact

with their environment and within themselves that they are able to acquire the needed resources and direct those resources into the re-constitution of themselves. Here re-constitution may range from ‘closed’ re-production of the system without change (modulo ‘copying errors’), at one extreme, to increasingly ‘open’ dynamic generation where system features change as a function of adaptive modification (including learning) and what primarily remains unchanged is simply the overall cohesion of the system-. Moreover, directive completeness, and hence autonomy, is subtly multi-dimensionally graded; humans internally direct the regeneration of their cellular organisation more strongly than do slime moulds in aggregation phase (cf. Herfel and Hooker 1998), but do not internally manufacture all of their essential amino acids whereas other systems do, though they can direct their acquisition of those they do not manufacture, and do rely more heavily on environmental cues to organise their interactive behaviour than do most (likely all) other species, though they often also direct the construction of these cues. Thus, while heterogeneous, autonomous systems form a typical (and typically complex) natural systems kind because underlying their surface variation these systems share this common holistic organisational feature which is fundamental to their existence.

This self-generation capacity constitutes the fundamental basis of

biological norms, on our account, because it marks the emergence of a ‘perspective’ (the continued persistence of the system) against which the outcomes of system processes are measured for success or failure. In section III this aspect of the theory of autonomy will be elaborated in an account of closure conditions — which are outcomes that autonomous system processes must achieve (e.g.

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adequate nutrition). The significance of this for developing models of adaptiveness is that the basic normative constraint on adaptive processes is a global one — they must interrelate in globally organised patterns focused on the autonomy of the system. All of the more specific normative constraints on particular actions (e.g. avoid hunger, pain) derive from this global constraint.5

Autonomy and autopoiesis. The centrality of directed interaction marks the

essential difference in orientation between autonomy and autopoiesis (Varela etal. 1974, Maturana 1981). Both concern open systems and their regeneration or ‘self-production’. But for autopoiesis the operative paradigm is one of an internally closed set of interaction processes, e.g. a system that can manufacture all its own distinctive components within itself (the ‘closed’ extreme above). Here imports and exports of matter and energy may be dynamically essential but do not participate in defining process organisational closure (see also Mingers 1995). By contrast, for autonomy the paradigm is the system that actively, directively constructs and/or compensates for external dependencies, and constantly changes itself as it manages its interactions to respond adaptively to its environment. Here the organisation of process closures essentially includes (some aspects of) the environment, but this is compatible with the internal locus of active, directive organisation characterising such systems.

For instance, the principal issue for Maturana in respect of multicellular

systems is whether they have the correct structure to manufacture all their own material components within themselves, not how well they cope with their environment, and he concludes that many may not be autopoietic. Indeed, the more complex they become the more stringent the autopoietic condition becomes and the less likely it will evidently be satisfied. By contrast, the interactive approach focuses on the way multicellular systems have access to far increased autonomy compared with their unicellular predecessors because of their enormously increased repertoire of interaction which results in a greatly increased adaptive capacity. Should local food fail them they can use specialised sensory cells to systematically search for more and, using specialised locomotor cells, do so further afield, and so on. Moreover, effectively exploiting such interactive advantages requires a central organising sub-system, the nervous system, and so stimulates, and is in turn reinforced by, the development of intelligent organisation. By contrast, while autopoiesis provides a basic organisational constraint wherever strict material reproduction must be met (and provided importation of externally manufactured components is allowed), pre-occupation with locating autopoietic closure in itself contributes little or nothing to understanding these basic evolutionary processes driving the emergence of neurally complex, adaptable life forms.

III: Autonomy as the foundational concept for an I-C theory of embodied cognition

This section outlines some of the main implications of our theory of

autonomy for understanding intelligence and cognition. We have developed the

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ideas in detail elsewhere.6 Here the focus will be presenting the basic conceptual structure and identifying some of the key differences from conventional cognitive science modelling approaches.

All living systems are autonomous, including bacteria and plants, so

autonomy by itself does not directly solve the problem of the origins and nature of cognition; it will require further work to embed cognition into an autonomous systems framework. We commence by noting that all adaptive systems rely on a capacity for directed interaction — action that shapes the interaction process in ways that achieve the closure conditions for autonomy. Directedness has several dimensions associated with its ‘steering’ capacity, including the ability to dynamically anticipate the interaction process, and the capacity to evaluate interaction using normative signals. These features of directedness are the ingredients from which cognition is formed. Thus our approach to cognition is a multifaceted one: there is no single ‘mark of the mental’, instead there is a group of capacities common to all adaptive systems that become specialised in adaptive strategies associated with intelligence, which itself retains this multi-factor character and appears in many varieties according to their interplay.

In most adaptive systems directed interaction takes only elementary forms.

Anticipation in mosquitos, for instance, is fairly primitive (see Klowden 1995), confined to local responses to chemical gradients and the like. Cognition arises through specialisation for a particular kind of adaptability we call self-directedness, in which anticipation and the integration of affective and contextual information is used to produce fluid goal-directed interaction. A good illustration of this type of ability is cheetah hunting, where the cheetah selects appropriate prey — young or weak animals are preferred — adapts the hunting technique to the context, e.g. using cover during stalking, and responds fluidly and anticipatively to prey behaviour, such as pausing if the prey looks up from feeding, or attempting to drive the animal away from the rest of the herd during the chase (see Eaton 1974). In certain circumstances self-directed systems are able to engage in a type of constructive learning process termed self-directed anticipative learning, in which the system learns about the nature of the problem as it tries to solve it. This type of learning underpins the capacity for flexible skill acquisition, and is the basis for generating improvements in cognitive ability. For

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example, as a cub a cheetah lacks most of the skills required for hunting and must acquire them through practice. This practice provides the maturing cheetah with important information about the significant factors involved in effective hunting, such as not breaking cover too quickly. As the cheetah gets better at recognising the relevant factors in effective hunting it becomes better able choose circumstances in which to hunt and to recognise sources of error in its hunting technique. Large brained mammals show evolutionary specialisation for self-directed learning ability, a trend particularly prominent in the massive cortical expansion and neoteny of humans which facilitates extended constructive learning. We will now outline the major elements of this picture.

III.1 Directed interaction: shaping the process flow The embodiment of an autonomous system as a cohesive structure

produces a continuous dynamical integration of the system’s internal and interaction processes. In a complex autonomous system there are a large number of processes which operate either in parallel or as part of a sequence, so the system faces a coordination problem – there must be mutual interaction amongst the various processes inducing each to continuously respond appropriately (including quiescence and activation as kinds of responses) to the overall context in such a way as to achieve those closure conditions which are prerequisites for maintaining the global coherence of system autonomy. In other words, the system must achieve an overall adaptive ‘process flow’. Directed interaction is the imposition, through shaped action, of dynamical constraints that ‘channel’ or direct the system’s processes so that the requisite closure conditions emerge, like the way that the banks of a river constrain the flow of the water. The connections between sensory and motor systems in a mosquito cause it to orient its flight path in the direction of high CO2 concentration, a simple but effective way of tracking CO2 gradients. When a cheetah chases its prey cascades of directed interactions are employed, from those resulting in the propagation of incoming retinal and proprioceptive signals and the ensuing complex affective and anticipative brain processing, to the outgoing signals sent along the motor system that modify electro-chemical potentials in the muscles, to the friction between paw and ground that propels the cheetah forward.7

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III.2 Dynamical anticipation An important part of directedness is a capacity to anticipate the dynamics

of the interaction process. Indeed, one of the key dimensions governing strength of directedness concerns the enrichment and expansion of the anticipative time-window within which the system is able to direct the interaction process flow (see Smithers 1995). Several of the most important kinds of process involved in dynamical anticipation include feedforward action, distal perception, memory, dynamical emulation, and imagination.

Feedforward action. Anticipative directed interaction is inherently

dynamical. In addition to the fact that it is realised in causal processes which operate in time, the anticipations have a natural time-scale determined by the cyclic nature of the interaction processes and the autonomous closure conditions of the system in which they are embedded. As claimed above, directed actions are basically feedforward processes in the sense that they modify the interaction process in ways which presuppose certain effects, in particular that the interaction with the environment will generate conditions appropriate for the system. As such even an elementary directed process in which a signal I initiates an action a involves a simple form of dynamical anticipation of the form: ‘Performing action a now (in response to the occurrence of signal I) will generate the closure conditions for a (feedback of type x within time-window tw)’.8 In effect the directed process involves a contextual heuristic temporal projection concerning the nature of the ensuing interaction process. In simple directed processes this anticipation is implicit in the process organisation, measured only by the health, and ultimately life or death, of the system, but in more complex directed processes at least some components of the dynamical anticipation can be enriched and made more explicit.

Distal perception. A simple but fundamentally import form of dynamical

anticipation involves distal perception and mobility. As Smithers (1995) points out, the presence of distal perception processes, e.g. vision, in mobile systems such as organisms and robots allows these systems to in a sense ‘see’ into the future, inasmuch as forward-looking perception provides information concerning environmental conditions with which the system will very shortly interact, thereby expanding the system’s ‘interactive present’. Thus, realised through its modulatory effects on the system’s motor and other processes, distal perception functions as a means for the system to project anticipatively into the future.

Memory and emulation. Memory processes, on the other hand, provide a

means to extend the interaction time-window into the past, allowing the system’s

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interaction history to have a modulatory influence on its current state. Memory can also facilitate dynamical anticipation by generating expectancies concerning regular relationships in the system’s interaction with the environment. This kind of learned expectancy can be realised in very simple conditioning processes such as the desensitization of a reflex.

More complex memory processes can facilitate more detailed forms of

dynamical anticipation, as in the case of off-line dynamical emulation. In many organisms neuronal systems involved in motor activity learn to emulate aspects of the dynamics of motor tasks such as reaching and grasping. These emulators are then able to supply context-appropriate directive signals more rapidly than is possible with sensory feedback loops. This process (also ubiquitous in control engineering) provides smooth and effective anticipative motor activity (see, e.g., Grush 1997). To illustrate the power of this form of dynamical anticipation, consider catching a ball. The most effective way to catch a fast moving ball is to anticipate the ball’s spatio-temporal trajectory and move so as to intersect it. Simply moving towards the current location of the ball will likely defeat the aim since by the time your hand gets there the ball will have moved on.

Imagination. Emulation processes can range from relatively contextual and

immediate feedforward motor signals to relatively more ‘offline’ imagination processes which can operate in the absence of overt behaviour. Imagination greatly enhances the capacity for dynamical anticipation by allowing the system to partially decouple its directive processes from the immediate context, permitting offline rehearsal and exploration of interactive possibility. The latter is particularly important, since opening up the capacity for modal anticipation permits high order cognitive processes such as resolution of competing goals and planning ability.

To sum up, increases in dynamical anticipation capacity enrich and expand

the system’s time-window for directed interaction, simultaneously reducing local context-dependency and improving context-sensitivity by allowing the system to shape its actions over longer timescales and with respect to more detailed, in some cases modal, information concerning the flow of the interaction process. As will be discussed below, these capacities are important for strong forms of self-directedness.

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III.3 Normative evaluation The range of modulatory signals a system has available to it plays an

important role in its capacity for directed interaction. Many plants, for instance, grow towards light, amoebae swim along pH gradients, and mosquitos fly up CO2 gradients. Amongst the array of modulatory signals that organisms typically possess are a special class concerned with normative evaluation. Evaluative signals provide information about the success of performance: pain, for instance, provides information that a current interaction has caused damage to the organism or, continued, will cause damage. Likewise, in the case of eating behaviour, hunger is an error signal indicating starvation whilst satiation is a success signal that indicates food acquisition has been adequately achieved. Such normative signals can be more or less action-specific. Thus, satiation is specific to food consumption (indicating success), whereas happiness is a less action-specific evaluative signal (it might be induced by many different kinds of activities).

The difference between relatively action-specific (low order) and

relatively non-specific (high order) normative signals is important for understanding learning because non-specific signals allow the system to modify its behaviour to better satisfy its constraints. For example, by modifying food intake to include foods with preferred flavour an organism can regulate its nutritional intake, such as when a chimpanzee seeks out fruit rather than just being content with eating plant pith. ‘Good taste’ here acts as a nonspecific norm for food intake, against which specific foods are evaluated and diet modified as appropriate. Part of this type of learning process can involve the construction of goals which improve the system’s capacity to satisfy its norms. A cheetah, for instance, may learn to hunt gazelle more frequently because it leads to greater satisfaction of hunger than other prey types, such as hares. By adopting the catching-of-gazelles as an acquired hunting goal the cheetah improves its ability to satisfy its more fundamental goal of avoiding hunger.9

III.4 Self-directedness We have been drawing a picture in which directed interaction is achieved

by the use of process modulation that steers the organism in its interaction with its environment. The nature of the process modulation is crucial to the degree of steering capacity afforded. For instance, mosquitos have simple low order process modulation that acts like a serial program, moving through sequences of stereotypical actions (tracking, feeding etc.), each of which is separately governed by simple modulatory signals. In contrast cheetahs possess much more

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complex high order process modulation that involves complex affective weighting over potential actions, producing more sophisticated context-sensitive choice behaviour, like hunting when hungry, except when there are pressing contingencies such as a nearby leopard that may threaten cubs.

This type of high order process modulation greatly increases steering

capacity and lies at the heart of self-directedness, which we believe forms the basis for the emergence of intelligence. Self-directed systems use anticipation and the integration of affective and contextual information to modify their actions in ways appropriate to the context so as to better achieve their goals. Mosquitos always track blood hosts in the same way, whereas cheetahs will continually adapt their hunting technique to the context in order to improve the chances of catching their prey. A primary adaptive specialisation involved in self-directedness is an enhanced capacity for learned anticipation. It is only by learning about, and anticipating, the characteristic relations in a complex interaction process that the system is able to effectively target its actions, particularly when there are extended temporal relations that are sensitive to small variations. If the cheetah breaks cover now the gazelle will be alerted at too great a distance, allowing it to prolong the chase to the point where the cheetah will become too exhausted if it continues.

As we pointed out above, part of this process involves constructing new

goals that improve the ability of the system to complete its tasks. The example above was of a cheetah learning to hunt gazelles rather than hares in order to better satisfy hunger. Another more sophisticated example is that of a detective conducting a murder investigation. The detective uses clues from the murder scene to build a profile of the suspect and then uses this profile to further refine the direction and methods of the investigation. The profile tells the detective what the murderer is like and what types of clues to look for. This in turn sets new intermediate goals which focus the investigation, such as search for organised crime links to the victim, and if the profile is at least partially accurate the modified investigation should uncover further evidence that in turn further refines the search process, ultimately (hopefully) culminating in capture of the murderer. It is the interplay between the discovery of clues, the construction of a suspect profile and subsequent modification of the investigation strategy that makes the process self-directing.10

III.5 Self-directed anticipative learning and cognitive development As should be apparent, learning and self-directedness are closely

interrelated. It is the ability of the detective to learn during the investigation

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process that permits the continual refinement both of her anticipations and of the search process itself, and that finally leads to the murderer. We call this type of learning process self-directed anticipative learning (SDAL), and regard it as one of the fundamental processes involved in cognitive development.11 The theoretical strategy underlying our formulation of SDAL is to move away from an artificial intelligence conception of learning as algorithmic problem solving in a formally characterised domain towards a conception in terms of functional problems which must be interactively solved in a natural context. Hutchins (1995) provides a good introduction to this type of approach.

In an SDAL process a feedback loop is established in which directed

interaction generates information that improves the system’s anticipation and thereby modifies the system’s interaction processes, generating yet more refined information, and so on. The system uses interaction to modify itself and/or its environment in ways that simultaneously move it towards its goal and improve its capacity to move towards its goal. The solution, the specific method for achieving it, and in some cases the proper formulation of the goal itself, are all progressively acquired. The detective’s investigation is an SDAL process. Another example is a young tennis player who employs a coach to improve her technique. The coach may observe that the player loses too many points at the net because of poor approach shots; the coach may then have the player practice her approach shot technique and teach her to only approach the net after a high quality approach shot. In this situation, a closure condition for effective net play is a good approach shot, but before the intervention of the coach the player was unaware of this condition. The coach, however, creates new goals for the player (hit good approach shots, only go to the net after a good approach shot) which make the previously implicit closure condition explicit. Because the tennis player is learning about the nature of the problem as she tries to solve it, her ability to learn improves. Once she becomes aware of the relationship between the quality of the approach shot and success at the net she is better able to assess strengths and weaknesses in her game, and this in turn can lead to further discovery, such as that she needs to mix net play with baseline play to add variety and make her less predictable to an opponent.

As the system interacts in an SDAL process its improving anticipative

models and interaction processes allow it to: a) improve its recognition of relevant information, b) perform more focussed activity, c) evaluate its performance more precisely and d) learn about its problem domain more effectively. Indeed, in this setting error itself can be a rich source of context-sensitive information that can be used to further refine these four

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features. The richer the system’s anticipative/normative structure is the more directed its learning can be, and the more potential there is that learning will improve the system’s capacity to form successful anticipative models of interaction. When successful, SDAL results in a pushme-pullyou effect as learning is pushed forward by the construction of new anticipations and pulled forward by the environmental feedback generated, creating an unfolding self-directing learning sequence. Because of its characteristic self-improvement SDAL can begin with poor quality information, vague hypotheses, tentative methods and without specific success criteria, and conjointly refine these as the process proceeds. This makes SDAL powerful because it allows successful learning to arise in both rich and sparse cognitive conditions.

Autonomy, dynamical modelling and robotics. Our approach to

understanding intelligence is naturalist; we regard all aspects of life as part of a single natural world and seek a unified understanding on that basis (Hooker 1987, cf. Christensen and Hooker 1998a). In particular, our models respect the requirement that all capacities attributed to systems should be shown to be dynamically grounded, in particular that adaptive and cognitive capacities should be based only on actually occurring dynamical system processes.12 However mainstream cognitive science and philosophy of mind employs an a-dynamical computationalist information processing conception of intelligent agents which is, if not explicitly anti-naturalist, at least very difficult to integrate with the broader biological context of intelligence as we currently understand it. From our perspective a positive development is that the increasingly visible limitations of computationalist information processing models has led to a resurgence of dynamically oriented modelling of cognitive phenomena. This includes the application of dynamical modelling in dynamical developmental psychology (DDP), e.g. studies of the emergence of crawling in infants as a dynamical bifurcation (e.g. Smith and Thelen 1993, cf. Hooker 1997); the emergence of a class of dynamical robotics models under the rubric of autonomous agent robotics (AAR), e.g. the attempt by Smithers 1995 to characterise autonomy in terms of the differential morphology of interaction fields; and the philosophical emergence of the dynamical systems thesis (DST), a more general defense of dynamical models as the appropriate foundation for cognitive modelling, exemplified by van Gelder’s holistic dynamical differential equation model of the Watt-steam-governor-and-steam-engine as paradigm for intelligent control (van Gelder 1995).13

In an obvious way we are sympathetic with this kind of approach to

intelligent agents (cf. Christensen and Hooker 1999b). However, if even roughly correct, our analysis also poses important theoretical and practical problems for

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dynamical and artificial models of intelligent agency, and we conclude by briefly discussing these issues.

According to our analysis the process organisation of systems is central to

understanding the nature of intelligence, but neither DST nor AAR possess the resources to capture system organisation. These approaches have tended to focus exclusively on the study of emergent dynamical patterns, their critical bifurcation points and control parameters and the like, using as the fundamental framework dynamical modelling of differential equations (d.e.s) as fields on differential manifolds, e.g. on system phase space. But these modelling resources, powerful though they are for modelling the energetics of processes, do not explicitly describe the physical organisation of the system – a chemical clock and a pendulum, for instance, may be modeled as equivalent dynamical oscillators. It is in the nature of specifying a phase space that only the global dynamical states and time evolution is specified, not the organised processes which produced it. So while it is always possible to capture the dynamical consequences of internal organisation by modelling system+environment as a system of coupled component subsystems, there is no principled, internally motivated basis for reversing the process to extract organisation, or for individuating the system in a principled way, or for specifying cognitively significant organisation in these terms.

In particular d.e./ phase space modelling cannot capture directed

interaction since the distinction between directed interaction and undirected interaction depends on the organisation of the system. It is for this reason, we believe, that van Gelder’s DST struggles with injecting any sense of the cognitive into its system models. The Watt steam governor does not provide the governor+engine system with any sense of directed interaction; it is not even a candidate proto model for an intelligent system. (That it may be input-output dynamically equivalent to an intelligent system under certain — very narrow — conditions is irrelevant, it performs only one function in a context-insensitive, self-insensitive manner.) It is instructive that in his more recent work articulating DST van Gelder expends much effort on attempting to clarify the concept of what it is to be a dynamical system, but sidesteps the issue of what it is to be cognitive and “simply takes an intuitive grasp of the issue for granted” (van Gelder 1998, p.619). However the question of what makes something cognitive is inextricably intertwined with questions about how agents work (are organised), there is no way to address one without addressing the other. van Gelder concedes that to capture cognition dynamical models will need to be “supplemented in order to provide explanations of those special kinds of behaviors” (1998, p.625). But he provides no guide as to what these “special kinds” of dynamical systems are or

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what kind of development DST might undergo in order to constitute a distinctively cognitive theory. Without such guidance the general dynamical modelling approach is evacuated of content.

Although our ability to characterise organisation dynamically is improving

(note 3), there is at present no obvious resolution to the general theoretical problem of how to incorporate organisational principles into dynamical models in a principled way. Resolving this problem is now one of our central theoretical challenges and its resolution will be of key interest to all sciences of non-linear systems.

However this turns out, cognitive organisation poses an immediate and

practical design challenge for roboticists who aim to design workable intelligent capacities into real ‘on line’ devices. Here the challenge is equally deep because, if our analysis of cognition is even roughly correct, it provides a set of organisational requirements for this task which will prove far from simple to meet. Despite the AAR label, e.g., there are at present no truly autonomous robots in our biologically based sense, as far as we are aware. There have been intimations — Grey Walter’s original 1940's turtle had a rudimentary autonomy function (it searched for energy outlets to recharge its batteries) and Brooks’ more recent Creatures were supposed to have ‘some purpose in being’ (1991, p.143). But adequately meeting this challenge will require more than that, more, indeed, than current robotics design methodology is used to considering. The criteria used to measure performance competency in autonomous robotics research are usually task specific, such as walking over irregular surfaces or navigating a cluttered room, and are determined intuitively by the researcher according to what seems important or interesting (as judged by the designer and/or buyer, not by the robot). This has not proved a major hindrance for robotics to this point since many requisite basic functional capacities are intuitively obvious (e.g. mobility, object manipulation etc.) even if the means to best achieve them are not. However the issue will become more pressing as robotics moves beyond the basic mechanics of independent behaviour to building sophisticated adaptable robots capable of efficient long-term functionality.

Intelligent autonomous robots will of engineering necessity have a

complex functional organisation, and they must perform adequately in a variety of tasks in complex variable environments without painstaking instruction or an extensive laboratory-like support system. Building robots capable of this type of performance will require a well-developed understanding of system-level adaptive management of complexity, including the active coordination of local and global functional constraints in a complex system and the capacity for action

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selection in the face of multiple alternatives. In living creatures this is achieved through the use of neural cellular behavioural plasticities of the most complex kind, delicately balanced between individual and integrated assembly influences; nothing comparable yet exists for manufactured robot bodies. Brooks’ Cog research (Brooks 1997) is one of the few robotics programs that is directly tackling these issues, and it is significant that the problem of motivation comes to the fore. Brooks argues that the humanoid robot Cog must have motivation that provides it with preferences over courses of action if it is to effectively choose from amongst several courses of action in a complex environment (Brooks 1997, p.298). Effectively Cog must, in our terms, be self-directed.

However Brooks’ recognition of the issues is incomplete; although he

clearly believes that norms play an important role in modelling intelligent behaviour, his specification of what they are is extremely vague. He says that Cog should ‘act like a human’ where this means, “roughly ... that the robot should act in such a way that an average (whatever that might mean) human observer would say that it is acting in a human-like manner, rather than a machine-like or alien-like manner” (1997, p.296). But if Cog really has motivation then it has internal norms, and we need to understand what these are and how they can be implemented. In our view the following issues must be addressed by AAR if it is to successfully build intelligent robots: 1) The way a robot can evaluate and modify its performance so as to satisfy, and perhaps learn about, its basic functional requirements. 2) The type of architecture required to perform high order (hierarchical and quasi-hierarchical) co-ordination tasks in a fundamentally dynamical, parallel processing context (cf. Bryson and McGonigle 1998). 3) The processes of solving vaguely specified problems, and of improving performance ability through skill acquisition.

IV: Conclusion

This paper has presented an interactivist-constructivist paradigm for

modelling adaptive intelligence based on a theory of autonomy. Autonomy as it is developed here is a characterisation of self-generating adaptive systems which possess normative process closure constraints. This root normative concept serves as the grounding point for modelling adaptiveness and intelligence. The I-C paradigm focuses on adaptive interaction rather than the internal computational processes modelled by conventional cognitive science. Intelligence is conceived as the ability of the system to adaptively direct its interaction processes in complex variable conditions. Cognition is seen as developing through constructive learning processes driven by the need to produce adaptive interaction.

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More specifically, intelligence is understood as emerging through increasing self-directedness. Self-directed systems anticipate and evaluate the interaction flow, directively modifying the interaction process so as to achieve goals which regenerate or improve the system’s autonomous closure conditions. Learning arises out of the drive to improve anticipation, which starts by being contextual, vague, and implicit, and becomes increasingly articulated and explicit as the system constructs anticipative models and goals for interaction. Cognitive development occurs through self-directed anticipative learning (SDAL), in which a pushme-pullyou effect is generated as increasingly rich anticipation increases the directedness of learning by improving error localisation, context recognition and the construction of improved anticipation.

References

Beer, R.D.: 1990, Intelligence as adaptive behavior: an experiment in

computational neuroethology, New York: Academic. Beer, R.D.: 1995, ‘Computational and dynamical languages for autonomous

agents’, in R.F. Port, T. van Gelder 1995. Bickhard, M.H.: 1993, ‘Representational content in humans and machines’,

Journal of Experimental and Theoretical Artificial Inteligence 5: 285-333. Brooks, R.A.: 1991, ‘Intelligence without representation’, Artificial Intelligence

47: 139-159. Brooks, R.A.: 1997, From earwigs to humans. Robotics and Autonomous Systems

20: 291-304. Bryson, J., and B. McGonigle: 1998, ‘Agent architecture as object oriented

design’, in M.P. Singh, A.S. Rao and M.J. Wooldridge (eds), The fourth international workshop on agent theories, architectures, and languages (ATAL97)’, Springer-Verlag.

Christensen, W.D.: 1999, An interactivist-constructivist approach to adaptive intelligence and agency. PhD thesis, University of Newcastle, Australia.

Christensen, W.D., C.A. Hooker: 1998a, ‘Towards a new science of the mind: Wide content and the metaphysics of organisational properties in non-linear dynamical models’. Symposium on Mind, Special Issue, Mind and Language, 13 #1, 97-108

Christensen, W.D., C.A. Hooker: 1998b, ‘From cell to scientist: Toward an organisational theory of life and mind’, in J. Bigelow (ed.) Our Cultural Heritage, Australian Academy of Humanities, University House, Canberra. Pp.275-326.

Christensen, W.D., C.A. Hooker: 1999a, ‘The Organisation of Knowledge:

153

Beyond Campbell’s Evolutionary Epistemology’, Philosophy of Science, 66 (Proceedings, PSA 1998), S 237-249.

Christensen, W.D., C.A. Hooker: 1999b, ‘Autonomy, heuristic policies and self-directed learning’. To appear in Philosophical Psychology 13(1), 5-45.

Christensen, W.D., Hooker, C.A. 2001, “A general interactivist-constructivist model of intentionality”, (in) MacIntosh, J, (Ed.), Contemporary Naturalist Theories of Evolution and Intentionality, Canadian Journal of Philosophy, Special Supplementary Volume, March 2001.

Christensen, W.D., J.D. Collier, C.A. Hooker: 1999, ‘Adaptativeness: Toward new autonomy- theoretic organisational foundations’. Submitted.

Clark, A.: 1997, Being there: Putting brain, body and world together again. Cambridge, MA: The MIT Press.

Collier, J.D.: 1988, ‘Supervenience and reduction in biological hierarchies’, Canadian Journal of Philosophy, supplementary volume: Biology and Philosophy, Calgary: University of Calgary Press.

Collier, J.D., Hooker, C.A.: 1999, ‘Complexly organised dynamical systems’. Open Systems and Information Dynamics, 36: 1-62.

Eaton, R. L.: 1974, The cheetah; the biology, ecology, and behavior of an endangered species, New York: Van Nostrand Reinhold Co.

Grush, R.: 1997, ‘The architecture of representation’, Philosophical Psychology, 10 (1): 5-23.

Herfel, W.E. and Hooker, C.A.: 1998, "From formal machine to social colony: Toward a complex dynamical philosophy of science”, in M. dalla Chiara etal. (eds.) Language, quantum, music: Select proceedings of the 10th international congress of logic, methodology and philosophy of science, Boston, Kluwer.

Hooker, C.A.: 1987, A realistic theory of science, New York: State University of New York Press.

Hooker, C.A.: 1997, ‘Dynamical systems in development: Review essay of Linda V. Smith & Esther Thelen (eds) A dynamic systems approach to development: applications’, Philosophical Psychology 10 (1): 103-112.

Hull, D.: 1988, Science as a process, Chicago: University of Chicago Press. Hutchins, E.: 1995, Cognition in the Wild, Cambridge, MA: MIT Press. Klowden, M. J.: 1995, Blood, sex, and the mosquito: control mechanisms of

mosquito blood-feeding behavior, BioScience 45: 326-31. Maes, P.: 1990, Designing autonomous agents: theory and practice from biology

to engineering and back. Amsterdam: Elsevier. Maturana, H.R.: 1981, ‘Autopoiesis’, in M. Zeleny (ed.) Autopoiesis: A theory of

the living organization, Boulder: Westview press. Mingers, J.: 1995, Self-producing Systems: Implications and Applications of

Autopoiesis. London: Plenum.

154

Rebek Jr., J.: 1994, ‘Synthetic Self-Replicating Molecules’, Scientific American 271(1): 34-40.

Rosen, R.: 1985, Anticipatory Systems: philosophical, mathematical, and methodological foundations, New York: Pergamon.

Smith, L.V. and E. Thelen (eds): 1993, A dynamic systems approach to development: applications’, Boston: Bradford/MIT Press.

Smithers, T.: 1995, ‘Are Autonomous Agents Information Processing Systems?’, L. Steels, R.A. Brooks (eds.) The Artificial Life Route to ‘Artificial Intelligence’: Building Situated Embodied Agents, Hillsdale, NJ.: Lawrence Erlbaum Associates.

Thelen, E. and L.V. Smith: 1994, A dynamics systems approach to the development of cognition and action. Cambridge, MA: Bradford Books.

Ulanowicz, R.: 1986, Growth and development: ecosystems phenomenology. New York: Springer.

Van Gelder, T., R. Port: 1995, ‘Its About Time: An Overview of the Dynamical Approach to Cognition’, in Mind as Motion: Explorations in the Dynamics of Cognition, van Gelder, Port (eds.), Boston: Bradford/MIT Press.

van Gelder, T. (1995) What might cognition be if not computation? The Journal of Philosophy, XCI(7), 345-81.

Van Gelder, T.: 1998, ‘The Dynamical Hypothesis in Cognitive science’, Behavioral and Brain Sciences 21(5): 615-627.

Varela, F., H.R. Maturana, R.B. Uribe: 1974, ‘Autopoiesis: The organization of living systems, its characterization and a model’, Biosystems 5: 187-196.

1. Department of Philosophy, University of Newcastle. S(nail)mail: Callaghan 2308, NSW, Australia, F(ax)mail: +612 4921 6928. P(hone)mail: +612 4921 5186. Email: plwdc [respectively plcah] @cc.newcastle.edu.au. The authors thank the editors and Mr. P. ‘Kepa’ Ruiz-Mirazo for helpful comments on an earlier draft that has led to substantial improvements in the presentation. 2. There have been a number of attempts to develop characterisations of the organisational basis of life related to the concept of autonomy outlined here, though there is considerable diversity in the details. Based on cells as paradigm examples, Varela etal. (1974), Maturana (1981) present a theory of autopoeitic, or self-reproducing, systems and Rosen (see, e.g., Rosen 1985, though his work begins much earlier) develops a mathematical theory of self-repairing systems he calls metabolic-repair systems. Bickhard (1993) contrasts energy well and far-from-equilibrium systems, and labels far-from-equilibrium systems whose identity is process-based self-maintenant systems. Ulanowicz (1986) and Smithers (1995), to our knowledge independently of each other, both speak of a class of autonomous systems described as self-governing. The conception of autonomy developed here is most influenced by the work of Rosen, Ulanowicz and Bickhard, however much of the detail of the analysis is original as we have sought a framework for understanding the evolutionary role of organisation and the

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origins of intelligence. The work in this section is drawn from collaborative research with John Collier (see references in text), and we wish to acknowledge the significant contribution he has made to the concept of autonomy as it is presented here. 3. Since we speak in this paper of order and organisation, we briefly characterise their technical meanings here. The root notion of order is that found in information complexity theory: the orderedness of a pattern is the inverse of the length of its shortest complete description or, equivalently, the orderedness of a correlation is the inverse of the number of entities which have to be specified to specify the correlation. Organisation is a particular kind of ordering. Gases are disordered and hence unorganised but regular crystals are highly ordered though very simply organised because their global ordering relation is highly redundant. By contrast (roughly) machines and living things are organised because their parts are relatively unique and each plays distinctive and essential roles in the whole, i.e. the system displays a non-redundant global ordering relation - though for this reason organised systems are less highly ordered than are crystals. A system’s organisational depth is measured by the degree of nesting of sub-ordering relations within its global ordering relation (cf. cells within organs within bodies within communities). Note that when we speak of high order features we refer to features characterised by ordering relations that encompass many nested sub-relations independently of whether they concern highly ordered features. In this sense living systems are deeply organised, and have many very high order constraints, processes etc. On the principled dynamical characterisation of organisation see Collier and Hooker 1999 and references. Finally, note that our specification of directive organisation is in terms of system autonomy; while we assume it to constitute an organisation, it is a very rich requirement that may well require much more relationally than the bare technical concept specified here. 4. This is ultimately the principled dynamical basis for any claim that species are themselves individuals, see e.g. Hull 1988. 5. This contrasts with standard models which characterise normative constraints locally: selectionist adaptive models in terms of separate correspondences between individual traits and environmental features, and computationalist information processing models in terms of self-contained input-output optimisation problems. And this is in turn but one aspect of a larger general divergence between currently standard and our dynamical-organisational approach: we adopt a system-oriented rather than a more abstract property-oriented form of analysis for fundamental concepts because understanding dynamical interactive relations within and between kinds of systems (in this case

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intelligent agents) is likely to be more illuminating than is abstract analysis of classes of properties (e.g. representation, rationality) considered independently of their embodiment and context. The systems approach reveals surprising interrelationships amongst properties associated with agency and intelligence which could not be expected from an abstract stance. On these divergences see further Christensen and Hooker 1998a, 1999b, c. 6. See Christensen and Hooker 1998a, b, 1999a, b, c. 7. In characterising these processes it is important to avoid applying the concept of control indiscriminately. The most specific, meaningful sense of control concerns the maintenance of a dynamical state through feedback indexed to a set point. It is common, however, to use ‘control’ as the general label for characterising the organisation of adaptive processes (e.g., “the engrailed gene controls segmentation specialisation”), but adaptive processes will take the form of full feedback control only occasionally. In many cases the normative closure conditions for an adaptive process are not attained through explicit error correction. They are instead often achieved through indirect directive shaping which induces organised outcomes (often relying on pre-existing order in the environment and system) without these outcomes being specified as internal reference conditions. The distinction is important since, as will be discussed in the text below, the way a process is organised to achieve its closure conditions has a significant impact on the system’s adaptive interaction capacity, affecting its openness, capacity to respond to variation, and capacity to learn. The risk when approaching the problem of adaptive behaviour from a highly abstract perspective is to assume — as do classical Cybernetics and Artificial Intelligence — that an adaptive outcome must be achieved through explicit control. But surfing may provide a better model for adaptive intelligence than Deep Blue calculating chess moves. 8. It must be emphasised that the formulation in the text is an approximation only and that anticipation in this sense is not basically linguistic in form, but rather has a non-propositional dynamical nature. 9. Our bio-chemical constitution and evolutionary history have combined to produce a relative paucity of natural high order norms. Good taste, for instance, is an imperfect proxy for nutritional adequacy (witness the range of nutritional deficiencies we suffer from poor diet choice). Part of the point of learning processes such as occur in science is to make closure conditions sufficiently explicit and accessible that we can develop surrogate norms for such conditions, and to develop the enlarged space and time windows that would permit extension of surrogate norms to such things as ecosystem health. Of course we then face the complementary issue of extending our natural motivating feelings, which derive from our natural norms, to our wider constructed norms, and unfortunately this often turns out not to be easy. 10. Science also illustrates these features — see Christensen and Hooker 1999a. From amoeba to mosquito to cheetah to human there is an enormous elaboration

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of all of the facets of directed interaction and of their interrelations. As we intimated at the outset, our view is that each lineage elaborates and combines these factors in its own idiosyncratic ways, so we have no simple hypothesis to present on the evolution of mind. But, very roughly, as cellular, and especially neural, complexity and organisation has increased over evolutionary time we see the appearance of increasingly organisationally powerful forms of cognitive organisation in at least some lineages, as indicated in the sequence above and its analysis. 11. A detailed discussion of the organisational features of SDAL is provided in Christensen and Hooker 1999b, the present discussion is confined to a qualitative outline of SDAL. 12. Surprisingly, this rules out many common assumptions, e.g. that proper function for a system is given by selection etiology or that primary signal meaning for a system concerns the state of the sender, since neither of these are, as such, dynamically available system conditions; see further Christensen and Hooker 1998b, 1999c. 13. On DDP see also Thelen and Smith 1994, on AAR see Beer 1990, Brooks 1991 and Maes 1990, and on DST see van Gelder and Port 1995, van Gelder 1998. Christensen 1999, chapter 1, provides further analysis of both AAR and DST from our perspective.