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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.

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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 that 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 that 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 that 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 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 that

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.

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Our approach is start from the ground up S 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 S self-structuring

far-from-equilibrium systems that seek out energy gradients that can maintain their dissipative

processes and also act to maintain and sometimes modify and elaborate the processes that enable

the exploitation 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 that 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 that individuate the system from

its environment (see Collier 1988, Christensen, Collier and Hooker 1999). These bonds fall into

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different organisational kinds S some cohesive systems are based on stable structural

relationships that cause the components of the system to bind together statically (e.g. rocks),

whilst others are based on process relationships that 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 that 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 that constrain the constituent molecules to spatial positions

within a crystal lattice. The bonds are localised in the sense that the strength of the forces that

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 S 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 that 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 that 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 that regenerate the cell

are disrupted.

Autonomous systems are cohesive systems whose organisation is of the same general type as the

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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 that 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, that exercise no self-regenerative regulation

(whence the determination of system features lie outside of whatever organisation there is), and

in degree from 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:

C Molecular catalytic bi-cycles: Mutually catalytic molecules that 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 that generates the process-patterning lies in the

macro-molecular conformation that catalyses the formation of the mirror template

molecule from the substrate material.

C 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.).

C 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

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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

C 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 pathways so as to preserve their functional

integrity, they have many processes that 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 that 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

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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 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’ reproduction where many

system features change as a function of adaptive modification (including learning) and what

primarily remains unchanged is this flexible capacity for re-constitution itself. 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

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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 that 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 S which are outcomes that autonomous system processes must achieve (e.g. adequate

nutrition). The significance of this for developing models of adaptiveness is that the basic

normative constraint on adaptive processes is a global one S 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 that

results in a greatly increased adaptive capacity. Should local food fail them they can use

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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 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 S 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

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this type of ability is cheetah hunting, where the cheetah selects appropriate prey S young or

weak animals are preferred S 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 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 that 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 that 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 that 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

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electro-chemical potentials in the muscles, to the friction between paw and ground that propels

the cheetah forward.7

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 that operate in real 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

that 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.

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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 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 that 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 that 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 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

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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, that 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 that 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 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

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of learning as algorithmic problem solving in a formally characterised domain towards a

conception in terms of functional problems that must be interactively solved in a natural context.

Hutchins Cognition in the wild (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) that 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 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,

16

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 that 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 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

17

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 that

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 S very narrow S 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 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.

18

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 that 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 S 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 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

19

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 that 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.

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 that 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.

20

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22

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 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 derived from algorithmic complexity theory: the

orderedness of a pattern is the inverse of the length of its shortest complete description.

Redundancy or correlation orders are determined by the minimal number of elements in which a

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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.

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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.

23

redundancy can be detected, order 1 redundancy can be detected by examining elements of a

system pairwise, whereas order n redundancy is detectable only over a minimum of 2n elements.

When we speak of high order features we refer to features characterised by high order relations,

relatively independently of whether they concern highly ordered features. Organisation is a

particular kind of ordering concerning relatively high order relations. 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. That is, an organised system displays a non-redundant global

ordering relation of relatively high order (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). In these senses living systems are deeply organised, and

have many very high order constraints (like global autonomy), 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 that 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 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 that 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.

24

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 that 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 S as do classical Cybernetics and Artificial

Intelligence S 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 S see Christensen and Hooker 1999a. From amoeba to

mosquito to cheetah to human there is an enormous elaboration 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

25

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.