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Lifelike Robotic Collaboration requires Lifelike Information Integration

Ron Cottam, Willy Ranson and Roger Vounckx

The Evolutionary Processing Group, IMEC Brussels

Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels

ron@cottam.be

Introduction

This paper is directed towards the definition

of a systemic characteristic suitable for the

“intelligent” core of a cooperative robot. A

major issue in developing cooperative

systems is the nature and degree of their

autonomy. It is rare (rather, impossible!) that

a communicated set of instructions for

carrying out a task will be exhaustively

complete, and with the passage of time the

instruction set and the task’s requirements

often diverge. Neither complete dependence

on control by a human “master”, nor

complete autonomy from control is suitable

or desirable within such a context, but it is

as yet unclear how a satisfactory contextdependent intermediate modus can be

developed which is conducive to

cooperation while not stifling any preexistent or nascent capability for creative

problem-solving. We believe that detailed

examination of a number of more general

aspects of system operation should predate

attempts at defining performance measures

for “intelligent” systems per se.

We wish to address four specific aspects of

the performance and visualization of large

information-processing networks. Firstly

(1), the character of information transport

through the networked connections of large

systems; secondly (2), the way in which

establishment of a hierarchical structure can

alleviate some of the resulting problems;

thirdly (3), the relationship between

rationality and emotion within such a

scheme; and fourthly (4), the manner in

which information is integrated and

visualized in human “thought” – which

brings us right back to our first chosen

systemic aspect (1).

Our starting point is the recognition that our

environment cannot be completely described

in any detailed manner by using a closed

formal system of rationality. The

representations we use for parts of our

surroundings are all to some extent

approximate in ways which relate to the

varying nature of their interactions at

different scales. Deviations from exact

correspondence between descriptions of an

entity at its different scales reside in the

inter-scalar interfaces, where interactions are

naturally complex and predictability is

limited. As a simple example of this

difficulty we can take a Boolean AND gate

with 2 inputs and 1 output. Conversion

between the 4 possible input states and 2

possible output states is controlled by the

logical rules which correspond to the predefined gate function, but even so the gate’s

operation is irreversible because information

is lost in the course of its operation.

Reversible state compression demands the

retention of all independent information, but

the only way an AND gate can be made

reversible is by recourse to non-local

memory... more of this later. In the

meantime we should simply note that

wherever there is cross-scale information

transport we can expect problems in the

application of closed formal rationality.

1. Large Systems

Looking into the heart of a system, we often

describe its pathways and their meeting

points by the simple picture of a network of
2

interlinked lines and nodes. An example is

the ball-and-stick models which are used to

describe molecules in chemistry. The balls

represent not only entities, but also

communicational nodes; the sticks represent

major communicational pathways. If all of

these pathways are similarly simply

specified, whether as globally existing or

globally absent, then the system is relatively

easy to describe, and it can be referred to as

being minimally complex. If, however, all of

the pathways are individually specified, then

the description is of necessity far more

complex. We must also decide very clearly

where we are looking at things from in

painting our picture, as in reality we only

have one point of view at one point in time.

A system can be described from an external

platform as a set of “order parameters”,

where accessible characteristics are purely

global ones, or from an internal point of

view, where accessible characteristics are

limited to local ones. It can also be

described from a quasi-external platform as

an externally viewed set of internal

relations. This latter picture corresponds to

just about every system analysis which we

carry out, but unfortunately in a system

which exhibits real scale effects internal

detail is inaccessible through the application

of formal rationality, or at the very least

only approximately (although conventional

science commonly presupposes this not to

be the case - and no, we have not forgotten

quantum mechanics here!).

So, our most usual “quasi-external” view is

self-contradictory in non-formally-rational

systems! We cannot equate the properties of

different scales of an even marginally nonformally-rational system, or even arbitrarily

change viewpoint within one and the same

scale level without addressing the associated

information transformations. Working from

the simplifying presupposition that nearby

viewing platforms will most resemble the

one we are currently standing on, we will try

to approach this problem by distinguishing

between directly and indirectly accessible

inter-elemental system connections 1. Direct

relationships are established by interelemental negotiation of both rationality and

context, directly and intimately between the

elements concerned, as shown in Figure 1.

Indirect relationships between elements are

those which of necessity pass through other

intermediate elements, which may then be

free to impose their own modifications on

forwarded information: choice of the

viewing platform imposes an asymmetry on

the resulting view. This problem makes an

appearance even in very simple systems: it

is the basis of the difficulty most usually

referred to as the Newtonian three-body

problem.

Direct relationships

established by

inter-elemental

negociation of

both rationality

and context

Indirect relationships

only accessible at the

viewing platform in a

form moderated by

the intermediate subsystemic elements

Viewing

platform

Figure 1. Direct and indirect relationships for a

3-body system with a chosen platform.

We can extend this distinction of direct and

indirect linkages to larger ball-and-stick

system models. Given 2 elements, we will

have 1 direct link and no indirect ones; with

3 elements there will be 2 direct links and 1

indirect; with 4 elements, 3 direct, 3

indirect; with 5 elements, 4 direct, 6 indirect,

and so on. As we move to larger randomlyconnected systems the relationship between

 

1 Clearly, the criticism we make here of “quasi-external”

viewpoints can equally be applied to the argument we are

ourselves presenting; but not, so far as we are aware, in a

manner which leads ultimately to its destruction: we are

trying to present a conceptual argument, and not a set of

formally related parameters.
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direct and indirect links takes on a clear

form: the number of direct links goes up as

the number of elements N; the number of

indirect links goes up as the square of the

number of elements N

2

/2, as shown in

Figure 2 (note that this effect is to some

extent alleviated in scale-free networks [1],

but that it never entirely disappears).

1

1 10 100 1e3 1e4 1e5 1e6

10

100

1e3

1e4

1e5

1e6

1e7

1e8

1e9

1e10

1e11

1e12

Number of system elements

Number of direct & indirect links

Figure 2. The growth of direct and indirect links in a

large multi-element system.

The populations of direct and indirect

character co-evolve at very different rates.

For a system with one million direct links,

there are a possible million-million indirect

ones: for large systems indirect links are

likely to dominate massively, depending on

the complexity of the relationship between

local and global structures. The character we

can attribute to a complete system is

ultimately controlled by this direct/indirect

balance, as is the robustness of a network

with respect to reductions in interconnection

viability. The co-evolution of direct and

indirect relations in large systems leads

ultimately to two different independent

systemic characters. One corresponds to the

"normally scientific" view, which depends

on formally-rational cross-scale information

transport, the other corresponds to parts of

the holistic system which are inaccessible to

a "normally scientific" viewpoint, and which

are associated with the distributed nature of

indirect relations. Complete representation

of systemic interactions with an

environment requires the evaluation of both

of these characters. If we simply describe a

quasi-externally viewed system in terms of

the reductively specified interactions we risk

missing out the majority of the systemic

character! (except if we are dealing with

time-independent (clocked) artificial formal

"machines" such as idealized digital

computer systems). We believe that it is this

bifurcation of systemic character into dual

reductive and holistic parts, and the

difference in rational accessibility between

the two systemic characters, which has led

to the conventional split between body and

mind, where the body is automatically

associated with direct "scientific" biosystemic relations and the "mind" is

naturally "difficult" to understand within the

context of a "normally scientific" rationality

which presupposes that all essential

systemic aspects can be related to a single

localized platform.

2. Hierarchical Stepping Stones

Large systems exist between two extremes:

their unification as a single entity and the

assembly of their smallest components. In

any system, natural or artificial, where the

spread in scale between these extremes is

very large, intermediate self-supporting

descriptive levels emerge (or are created) to

facilitate transit across the entire scalespread of the system (e.g. stairs,

semiconductor inter-band traps, VLSI

design, stars in the universe, ...). This aspect

of large systems is so pervasive that we can

formulate a universal model for the resulting

hierarchical systems [2] (Figure 3), whose

properties are very closely tied in with the

arguments of Section 1 above. Here each

level of the coupled "model" hierarchy

represents one and the same entity, for

example a tree. Successive hierarchical

levels describe the entity with a

progressively changing degree of detail,
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from the most elaborate to the most simple.

For example: ..., a tree as atoms, a tree as

molecules, a tree as cells, a tree as

branches, a tree "as itself", ... These

successively simpler representations of the

entity "contain" progressively more and

more sub-scalar detail which is hidden from

locally-scaled ecosystemic interaction.

N

onlocality

Localization

Newtonian

potential

wells

Dimensionally reducing

overall complexity

Reducing digital complexity

Increasing digital complexity

CONTAINING 

CONTAINED 

analog

complexity

digital

complexity

N

onlocality

Localization

Newtonian

potential

wells

Dimensionally reducing

overall complexity

Reducing digital complexity

Increasing digital complexity

CONTAINING 

CONTAINED 

analog

complexity

digital

complexity

Figure 3. A generalized complementary hierarchical

evolutionary system.

At a given level of the model hierarchy, the

relevant representation provides a partial

"en-closure" of sub-scalar detail, leading to

simplification of the relationships between

the entity and its locally-scaled environment

as a trade-off against representational

precision. Possibly the most important

aspect of natural hierarchies is just this, that

through the establishment of a series of

related progressively abstracted models of

low-level detail, high level "forms" are not

constrained to operate within the complex

temporal limitations of their low-level

interactions. The apparently most simple

model is the one which contains the most

hidden sub-scalar detail. Within a

computational paradigm this "hiding" of

sub-scalar detail makes it possible for

biological organisms to develop mechanisms

for multi-temporally-scaled reactions to

external stimuli, promoting survival in a

complex hostile environment. Our own

brains use a mechanism of this kind in the

context of "fear-learning" [3].

The model levels appear as Newtonian

potential wells in an otherwise nonNewtonian multi-dimensional and multiscaled phase space between nonlocality (on

the left) and localization (on the right) [4].

Moving between adjacent model levels, we

encounter both kinds of complexity (digital

and analog). Towards the left hand side of

the assembly models are related to a global

conservatism, and towards the right hand

side to a local causality: the assembly forms

a coupling structure between these two

aspects of nature. Movement through

different model levels towards the right

corresponds to a reduction in the containing

digital complexity of models and an increase

in the contained digital complexity. It is

worth adding that the Newtonian potential

wells which correspond to the different

model levels are regions of the universal

phase space where global and local effects

are self-consistent. This is a fundamental

aspect of the stability and computability of

nature. A major consequence is that, within

these Newtonian regions, local causal

interactions can proceed within limited

temporal scales without fear of contravening

a more global conservatism. However, the

viability of such a structure as a general

model of hierarchical systems depends on a

fine balance between the isolation or "enclosure" of adjacent levels with respect to

each other and the degree of intercorrelation which is necessary to support

their stability and that of the hierarchy: a

degree of inter-level correlatory information

transport is vital: too much is fatal!

3. Ecosystemic Rationality and

Emotion

The significance of the general hierarchical

assembly we propose is that it represents the

intermediate structure which we can
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observe, not only between the

complementary pair of “extra-real”

nonlocality and “extra-real” perfect

localization, but between and internal to any

and all high-level "extra-real" complements.

We can find complementarities of this kind

everywhere as soon as we start looking for

them: quantum and classical descriptions;

organisms and their ecosystems; ... There is,

however, a further complementary twist to

the story. The hierarchical assembly we

propose dissociates into two distinct and

complementary systems of rationality.

Closure of rationality-pairs

Combined result

Figure 4. Interleaved “normal” and “complementary”

rationalities in a generalized birational entityecosystem representation associated in local

birational pairings.

One, of “normal” rationality, is associated

with the Newtonian potential well model

levels, and is reductive towards perfect

localization. The other, of “complementary”

rationality, is associated with the

intermediate complex regions, and is

reductive towards perfect delocalization [5].

The two systems are interleaved to give the

complete structure which we showed earlier

in Figure 3. It does not appear accidental

that this binary complementary structure

matches that of quantum-holographic

vector-reconstruction information

processing [6]. The result is a set of lowlevel local complements where a "normal"

(local) level is always associated with a

(local) ecosystemic "complement" level, as

shown in Figure 4. The summation of both

levels at any scale results in complete

systemic description: the ecosystemic level

provides a local but “normally inaccessible”

store for all of the information which is

eliminated through the “formally rational”

compression to the current level through the

stepping stones from the lowest level

description (see the argument about a

Boolean AND gate in Section 1).

Interestingly, and probably unavoidably, this

situation corresponds closely to the two sets

of different information which are invoked

during quantum error correction techniques.

“Normal”

rationality

“Complementary”

rationality

“Complementary”

cross-scale

“Normal”

cross-scale

Figure 6. Interleaved “normal” and “complementary”

multi-meta-scaled cross-scale rationality systems,

based on their interleaved “normal” and

“complementary” model assemblies.

We should remember that the Newtonian

hierarchy must be globally stabilized by

interactions right across the system between

all scales. Noting that by “understanding”

we usually imply “seeing the relationships

between the level we are talking about and

both higher and lower adjacent ones”, we

believe that inter-scalar interactions generate

first a “hyper-scale” descriptive level, and

then progressively a hyper-scalar hierarchy

superimposed on the initial scalar one. A

similar interaction for the “complex”

hierarchy results finally in a pair of hyperscale hierarchies, as shown in Figure 5.

It should be noted that at the highest hyperscale levels, correlation between the

developments of the two systems becomes

increasingly less relevant, as the two

structures progressively separate from each
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other in association with the degree of their

individual abstraction. At the highest levels,

each of the hyper-scale manifestations is

clearly and distinctly independent, and

identifiable with a different kind of

rationality [7].

This corresponds to the conventional picture

we hold of our own thought processes; on

one side there is a “rationality” which

corresponds to scientific logic, on the other a

very “difficult-to-categorize” but effective

“irrationality” we refer to in terms of

“emotion”. The two are complementary:

failure of (rational) logic in pragmatic

situations can be circumnavigated by

recourse (irrational) emotion; failure of an

emotional approach can be rectified by the

application of logic. Neither one can

successfully exist alone: reason needs

emotion, emotion needs reason. Our

civilization habitually focuses on only one

of the pair: reason is everything;

emotionally, even, we feel we “should” be

logical!

4. Information Integration and

Consciousness

As we all (sic) were taught in school, the

mammal eye works to create an inverted

image of the viewed scene at the retina

(whose orientation is rectified by the brain).

Not so. There is no integrative capability at

the retina to perform this function. Any

“image” is generated much later, in the

various layers and centers of the brain: it

only “exists” within the (abstract)

unification of high-level consciousness, and

never in any “real” sense describable by

science. If you are viewing this text via a

computer screen or through the printed

word, the same constraint holds: it does not

exist at all as a unified entity outside your

brain or imagination, merely as a collection

of informational elements devoid of any

implicit organization, which was transmitted

through the Internet by a means which has

been formally (scientifically) structured

through the application of our imagination to

achieve our aim of reproducing arbitrary

“patterns” across space and time. The same

argument holds for the entirety of our

environment: it is all beyond representation

by (current) science. Not only does this

description apply to “objects”, it applies

equally well to any and every subject of

discussion.

Most particularly, in the current context of

interest, we should not expect to find that a

robot is capable of responding as a “black

box” to external stimulus on the basis of an

internally integrated “motive”, except where

that “motive” is completely relatable to its

formally unified degenerate representation –

namely the binary “it exists” or “it doesn’t”!

Such a quasi-hierarchical relationship (along

with any “algorithmic” complexity it

exhibits) is both nominally and functionally

trivial when compared to the styles of real

complexly-hierarchical operation which

characterize living organisms. We should

consequently beware of attributing

anthropomorphic integrative unification to

the internal workings of a “black box” robot

unless it is entirely predictable (a character

which corresponds exactly to the quasihierarchical condition referred to above), in

which case any resemblance of its actions to

those of a human is far from likely, to say

the least! So, can we describe and develop

robots “in our own image” by the

application of scientific techniques, or not?

Or does the problem which must be

addressed reside elsewhere?

Descriptions of the natural world and the

placing of robots within it which derive

from Evolutionary Natural Semiotics (ENS)

by way of signs are untouched by this

dilemma. In the context of ENS, any

formalized representation is derived

pragmatically (but less-than-algorithmically)
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from its own scale-local grounding, and the

various scale-localizations are coupled

through and within the context of a globalto-and-from-local correlation which

mediates between the scale-local groundings

of a global grounding which it also creates

(!). “Reality” (in a scientific reductionist

sense) then refers to nothing more that the

lowest level of description which we can be

bothered to deal with, whether that be the

atomic level, super-strings, membranes,

people, trees or psychological states.

The descriptions which we habitually

employ for “systems” which are internally

structured in a network-like manner are

suitable if, again, the network structure is

amenable to complete (formal) integration

reductio ad adsurdum, but for a “system”

which exhibits “useful” complexity, they are

worthlessly simple or simplified. Within

ENS such representations (where we view

the “system” as a whole and simultaneously

its network-like internal structure) have the

character of (dubious) quasi-external

representations, whose (cautious)

applicability depends primarily on their

degree of representational equilibrium.

Much effort is currently being expended in

developing “internalist” models of

operational situations, rather than the

“externalist” ones said to be characteristic of

scientific endeavor. It is difficult to imagine,

however, how a uniquely internalist

representation of a “conscious” or aware

state can or could be useful: its existence

would imply not only the usually-quoted

criterion of lack of knowledge of the causes

of received stimuli, but also the complete

absence of any attempt to investigate or

imagine the origins of those stimuli. To

investigate in such a way requires the

construction of an (imagined) externalist

model of the situation: to not do so seems to

imply lifelessness! Consequently, it makes

more sense to describe living interactions as

a negotiation between internalist and

externalist representation, through a process

which mirrors the internal-external

negotiations which lie at the roots of human

consciousness and moderated autonomy [8].

Human consciousness is “singular”, in that it

only exists as an individual unified “entity”.

It is within the “sufficient interpretation”

and correlation of a multiplicity of

informational details that this text becomes

(nothing more... just “becomes” itself)

within our consciousness. Its existence

emerges from the process of integrative

interpretation (or interpretive integration, if

you prefer). This process, of the emergence

of the informal from the formal

(simplistically describable as emergence of

the analog from the digital), is the very

nature of living entities. It appears most

obviously, but not uniquely, in the

generation of analog protein folding from

the digital code of DNA. Science does not

merely omit this emergence from its

confines; it expels it, as being too difficult to

deal with. A lifelike nature is by definition

external to a scientific development!

Cooperative Intelligent Systems

So, how are we to develop “lifelike”

cooperative intelligent systems? Ultimately,

not through uniquely digital computation,

although this can provide effective

interfacing between a central information

processor and the outside world. This is

itself the manner in which our own brains

operate: a central really parallel processing

style, whose operation is most closely

related to the superposition-and-selection

mechanisms of quantum mechanical

interaction [9, 10], and integration and

differentiation of the results of this

processing to serve localized output and

input nodes. Currently this style of

integration and differentiation is far beyond

our constructional capabilities, and while a

prime target must be to investigate and
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develop lifelike information integration, we

can nevertheless achieve useful preliminary

results if we couple our targets to the means

which are available, so long as we do not

fool ourselves into thinking that this will be

sufficient.

Two routes (at least) present themselves for

simulation of a desirable computational

structure. One depends on the axonite mesh

proposition of Karl Pribram [10], in which

the outputs from a large number of senderneurones are distributed in parallel as a

quasi-wave to a large number of receptorneurones, simulating the nonlocal

distribution of solutions which characterizes

quantum superposition, and storing the

associated information in a distributed

manner as a “collapse” of the wave at the

receptor dendrites. This strong contender

matches well with experiments carried out to

define the neural location of consciousness

[11]. The other route depends on the

mathematical distribution of information

across a large parallel processing network by

the recursive integration of Dempster-Shafer

probability into diffuse rationality [12]. It

remains, however, difficult to see how either

of these routes can provide a sufficiently

“intelligent” information integration to

generate any “real” consciousness in an

artificial structure, and long-term hopes

most probably rest with currently advancing

projects which aim to introduce less-thanformal computation into the hardware

elements of computer processing, rather than

with the simulation of parallel processing

via digital software.

References

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th

International Conference on Complex

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Forthcoming publication.

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