Self organization and human robots by fiona_messe

VIEWS: 6 PAGES: 7

									SELF-ORGANIZATION
Self-Organization and Human Robots

Chris Lucas
CALResCo Group
http://www.calresco.org




Introduction
Humans are rather funny things, we often tend to imagine that we are so ‘special’, so divorced by our
supposed ‘intelligence’ from the influences of the ‘natural world’ and so unique in our ‘abstracting’ abilities.
We have this persistent delusion, evident since ancient Greek times, that we are ‘rational’, that we can
behave as ‘disinterested observers’ of our world, which manifests in AI thought today in a belief that, in a
like manner, we can ‘design’, God like, from afar, our replacements, those ‘super-robots’ that will do
everything that we can imagine doing, but in much ‘better’ ways than we can achieve, and yet can avoid
doing anything ‘nasty’, i.e. can overcome our many human failings - obeying, I suppose, in the process,
Asimov’s three ‘laws of robotics’. Such human naiveté proves, in fact, to be quite amusing, at least to those
of us ‘schooled’ in AI history. When we look at the aspirations and the expectations of our early ‘pioneers’,
and compare them to the actual reality of today, then we must, it seems, re-discover the meaning of the word
‘humility’. Enthusiasm, good as it may be, needs to be moderated with a touch of ‘common sense’, and if
our current ways of doing things in our AI world don’t really work as we had hoped, then perhaps it is time
to try something different (Lucas, C., 1999)?


From Control to Freedom
The traditional AI approach, being a top-down process, echoes the general behaviours seen in our world
today, which attempt to centralise power and to have one ‘designer’ (or a small group of them) create a
‘robot’ or ‘system’ based upon a specification of some sort or another. In other words we need to first decide
what ‘someone’ wants to achieve and then to implement or to impose a way of arriving there. Unfortunately
the success of this method has been rather slight in practice, we still don’t know enough about the basis of
intelligence to ‘design it’ effectively - especially if we wish to mimic what humans actually do ‘well’, rather
than what they do ’badly’ (which we can, rather irrelevantly, just ‘manage’, it seems, to do artificially!).

As a way of overcoming the limitations of this ‘outsider’ method, an alternative has been proposed, i.e. the
subsumption architecture of (Brooks, R., 1990). Here we concentrate our attention on a number of relatively
simple operations, for example ‘moving forward’ or ‘turning’. Each of these is implemented in an
autonomous module and these are then arranged into a layered hierarchy, with the most ‘primitive’ at the
bottom. Each module can then inhibit the higher (more valued) modules, whenever their lower function is
needed or is ‘necessary’, in other words we have ‘priority interrupts’. In this way we can avoid the need to
plan out exactly what should happen in every possible scenario, instead we leave it to the environmental
feedback to ‘select’ (evolutionary fashion) which ‘specific’ module needs to be operational at any time and
for how long. Whilst this has proved quite successful, allowing the emergence of unexpected behaviours that
rather look ‘intentional’ - although these systems are teleonomic not teleological, (so it is only the ‘observer’




064
that imputes ‘intention’ to them), we find that significant limitations still exist, e.g. in the need for explicitly
‘designed’ operations.

Given that this architecture relies on the environment, but still requires that each module be ‘hand-crafted’,
can we go yet a stage further and dispense with that ‘designer’ stage, allowing feedback itself to ‘sculpt’ the
robot entirely, i.e. in the same manner as is thought to happen in evolutionary biology? In traditional neo-
Darwinian evolution (Futuyma, D.J., 1986) we rely on genetic mutations to generate the variety on which
selection then acts. This however is a very long term process, it took billions of years before single celled
organisms (e.g. bacteria, which reproduce every 20 minutes or so) achieved multicellularity, and a billion
more years were needed before ‘intelligent’ creatures arrived as a reality. It is most unlikely that any of us in
the AI community could live long enough to achieve even a basic prototype!

Fortunately we can take advantage of some short cuts. One of these acts by using computers, in order to
model the multi-generational ‘phylogenetic’ evolution scenario just outlined. We can, using high speed
computers and a technique called ‘genetic algorithms’ (Holland, J., 1992; Lucas, C., 2000a), operate at
speeds of hundreds of generations per second - increasing as computers become faster. In this way we are
able to ‘evolve’ some structure in a reasonable length of time, but what this happens to contain depends very
much upon the constraints we apply to the system. These constraints mimic the ‘selection’ phase of natural
evolution, but what should they be? Once again, in order to get what we want, so that we can arrange to
‘select’ for it, we need to know what it is in advance - so how can we avoid designing our ‘fitness function’
appropriately, but so deterministically, and then suffering the problem that evolution stops altogether once
the population has ‘converged’ on our fitness optimum?

One way around this new problem is to use coevolution. This means that we let one ‘organism’ act as the
fitness function for the other, and vice-versa. By using this sort of technique we can indeed generate
improved functionality, for example in deriving efficient ‘sort programs’ where the list of numbers to be
‘sorted’ also evolves in difficulty (Hillis, W.D., 1991). But we have to start somewhere! We must create at
least ‘prototypes’ of program and list, before anything at all can happen. So we seem yet again to be forced
back to stage one - deliberate design. We can minimise the extent of this to some degree by simply
designing-in the ability to adapt to unpredictable environments, as in ‘reinforcement learning’ (Ackley, D.
H. & Littman, M. L., 1992), but the basic ‘intelligence’ to so act must still be ‘crafted’ by our ‘outsider’.


Growing from Scratch
But if we can’t find a way to get over this problem then how can ‘nature’ possibly do so, do we need a ‘God’
as some seem to think? Here we come to the crux of the matter, in considering a process that has been left
out of standard neo-Darwinian evolution, that of ‘development’ or ‘ontogeny’. Every human ‘robot’ starts
life as a single cell (like our bacteria), this then grows into an embryo and thus eventually is born as a (more
or less) functioning human child. But that is not the end of the matter, life experiences then develop,
initially, the brain (our neural network wiring) and later (in interactions with others) our mind or
‘personality’, i.e. our behavioural range. It is this latter state we hope to duplicate in AI, so looking at how
this process is understood in developmental biology should prove to be a useful indicator as to how we
might achieve, artificially, much the same result, and without the need for any form of external ‘intelligent
designer’.

Unfortunately it isn’t understood very well at all as yet, at least in overall terms! Embryogenesis, as it is
called (Bard, J., 1990; Slack, J.M., 1991; Wolpert, L., 1998), consists of three stages. Firstly ‘growth’ (the
duplication of cells by asexual or ‘mitotic’ reproduction), secondly ‘differentiation’ (the spliting of cells into
different tissue types) and thirdly ‘morphogenesis’ (the creation of form or structure). The first stage
expands possibility space, for every doubling of the number of cells we have a combinatorial explosion, e.g.
for just 64 cells we have over 1089 permutations (64!), and this escalates rapidly during the growth process.
But let us look more closely now at the second stage, after all, these permutations only become really
interesting if the cells can be distinguished from each other. What is known about this is that there are many
genetic regulatory networks involved, each ‘cell type’ (and there are hundreds) activates a different set of
genes. These regulatory ‘controls’ do not however operate in a linear hierarchy, 1:N, in the way that we


                                                                                                                065
often regard human control networks, nor do they have fixed functions, independently maintained, as we
often consider in AI ‘subroutines’.

What happens is that they are arranged into a web, in N:M fashion, where each ‘gene’ inter-links with many
others, switching them on and off and being controlled in a like manner (Lucas, C., 2004b). This is a
‘circular feedback’ form of causality, called ‘polygeny’ and ‘pleiotropy’, which actually operates on several
different levels. The same processes of activations and inhibitions also take place between cells (the third
stage of embryogenesis), and this includes the inter-neuron connections in our mind. We can even, if we
wish, go beyond the mind as an entity in itself and venture out into the wider world, and we shall see exactly
the same phenomenon, in society and in ecology both, i.e. a ‘systems’ viewpoint of some type is applicable
in all areas of our world.


Self-Organization Arrives
What is common then to all these processes and levels? Well, it is the idea of ‘self-organization’ which is
our focus in this article, which we can define like this (Lucas, C., 2004d):

        “The essence of self-organization is that system structure often appears without explicit
        pressure or involvement from outside the system. In other words, the constraints on form
        (i.e. organization) of interest to us are internal to the system, resulting from the
        interactions among the components and usually independent of the physical nature of
        those components. The organization can evolve in either time or space, maintain a stable
        form or show transient phenomena. General resource flows within self-organized systems
        are expected (dissipation), although not critical to the concept itself.

        The field of self-organization seeks general rules about the growth and evolution of
        systemic structure, the forms it might take, and finally methods that predict the future
        organization that will result from changes made to the underlying components. The
        results are expected to be applicable to all other systems exhibiting similar network
        characteristics.”

In other words, we now have a method of generating demonstrable structure for ‘free’ (Heylighen, F.,
1999), of getting over (maybe) our primary ‘design’ problem. To see just how significant this is, let us add a
few numbers. Suppose we have 10,000 randomly connected, 2 input, logic gates (what is called a ‘random
Boolean network’ of size N - Lucas, C., 2002b), in other words the number of different possibilities is 210,000
- a staggering number. On average (with considerable variance) we would expect any area of the self-
organized system to only visit (cycle through) 100 (square root of N) different dynamical states - this is less
than 27, and there will be (again, with considerable variance) only about 100 disjoint areas of activity, 27
again (root N). So the ratio of initial ‘disorder’ to final ‘order’ is a massive 29986 or so ! This is not quite such
a ‘magic’ solution as we may wish however, we still must have ‘something’ initially to work with (the gates
here), but this proves to be our second ‘short-cut’. If the ‘lower levels’ also emerged (as we think) in this
way, then we can ‘cut to the chase’ as it were, and ignore how they actually got there. We thus can start off
with ‘parts’ of appropriate types for our AI purposes, called ‘agents’, and let a collection of these coevolve
and self-organize, bottom-up, to meet our needs. All we have to do then is to sit back and watch it happen...

Studies of the dynamics of such scenarios (Kauffman, S., 1995) show that three general results are possible.
In the first, the agents are insufficiently connected (too cool), they don’t interact much at all, so the system
quickly settles into a fixed state, we have convergence to a ‘static’ result (akin to the traditional single
analytical ‘solution’ in science). In the second the agents are highly connected (too hot) and each affects
many others constantly, here the system cannot settle, it is always ‘perturbed’ (Lucas, C., 2000b) and
exhibits a ‘chaotic’ behaviour (those ‘insoluble’ systems usually ignored in science). In the third state,
which I call ‘Type 4 Complexity’ (Lucas, C., 1999), we have a (just right) behaviour that modularises the
system, with some sets of agents proving to be static, some chaotic, and some dynamic - many of which will
swap places over time as the system evolves between the possible (semi-stable or multistable) ‘attractors’
(Lucas, C., 2002a). In this scenario we find that the maximum ‘fitness’ can be achieved, the best overall


066
performance is within reach. For larger systems, the dynamics will achieve a ‘fractal’ or ‘power law’ spread
of properties, called ‘Self-Organized Criticality’ or ‘Edge-of-Chaos’, (Bak, P., 1996; Lucas, C., 2000b),
which can give a somewhat emergent multi-layered or hierarchical structure, with inherent cooperative
behaviour between the parts becoming apparent (Ünsal, C., 1993).

This idea of appropriate ‘connectivity’ is proving to be highly important in many areas of our world, from
the social ones related to ‘anarchy’, ‘democracy’ and ‘totalitarianism’, via medical ones related to
‘epidemics’, through to ecological ones related to ‘diversity’ and freedom of combination, not to mention
the physics or mathematical ones related to ‘spin glasses’ or ‘percolation’. By arranging connectivity
suitably we can enable our important self-organizational processes. This is the ‘communications’ aspect of
agent interaction, but two other aspects also need to be included here if we are to achieve success in our
self-organizational scenarios. The first is appropriate size, relating to decentralisation. Systems must be
small enough to be self-contained - if they are too big then the inertia of ‘bureaucracy’ inhibits the
recognition of any ‘improvement’; but they should not be too small either - else they will have insufficient
‘variety’ with which to make improvements. The other aspect is ‘stress’, a desire or a need for improvement.
Again if this proves to be too high the system will disintegrate, we will have rapid breakdown; but if the
stress is too low then the ‘status-quo’ cannot be overcome and a static state will persist. Given that these
‘middle-way’ conditions are met, then self-organization should occur and the system will generate our
required ‘novelty’ or emergence.


A Competitive Problem
Although this explanation sounds very glib and easy, in practice there are a number of problems, for
example in the (very visible) social and environmental destructiveness that we can see around us, resulting
from the unfettered individualism driving the self-organization (‘invisible hand’) of the (over-stressed) ‘free
market’. Experiments using Multi-Agent Systems (MAS), which operate using these ideas of self-
organization, have also not so far achieved those higher levels of structure that we so desire and expect
(seen in nature in the progression atoms-molecules-cells-organisms-societies-ecologies), and which are
commonly to be found in the behaviour of ‘swarms’, for example, in insect societies (Bonabeau, E. et al.,
2002), where ‘stigmergy’ (environmentally mediated communication) also has an important effect (Holland,
O. & Melhuish, C., 1999). These current failures may well be because of the assumptions embedded within
the agent structures typically used. In so many current systems, there is an inherent ‘competitiveness’ -
echoing the belief behind the phrase ‘survival of the fittest’ often employed by Darwinists (and capitalists).
Yet let us consider cellular development once more, what would be the effects of such ‘competition’? The
answer seems pretty clear, it is the same as what happens when we suffer from ‘cancer‘ - the competition
from ‘rogue’ cells eventually destroys the host. Thus it is not ‘competition’ that we need, but ‘cooperation’.
We need to find a way for the agents to ‘work together’, since only in this way can organisms (and/or
societies) function and persist at ‘higher levels’.

The principle we are looking for, which we wish to employ within our self-organizing systems, is called
‘synergy’ (Corning, P., 1995; Lucas, C., 2004c). Here, when two or more agents come together, a new
‘functionality’ arises, they gain combined powers greater than the sum of their separate powers, often
illustrated with the phrase “the whole is greater than the sum of the parts”. But how can this possibly work?
In essence, by a form of combinatorial trial and error - in which, in the processes of interaction, this new
higher level functionality arises. Thus there is initially a diversity and an ongoing novelty, as seen in the
pairwise encounters of the heterogeneous agents, but in some way these agents then ‘associate’. This, like
the sexual crossover experienced in evolution, allows new ‘building blocks’ to arise, new combinations of
functions which may, perhaps, operate in an entirely new way - we have potential ‘emergence’ (Lucas, C.,
2004b). In the operation of the typical MAS the agents interact and learn (at least partly) in a random
fashion, and their individual behaviours change, but they do so not only as a result of their own experiences
- we find instead that the ‘higher level’ places new constraints upon them (Epstein, J.M. and Axtell, R.,
1996). These constraints, called ‘downward causation’ (Campbell, D.T., 1974; Lucas, C., 2004b) add new
values to the system, new environmental relevances at a ‘group’ level that imply selective forces beyond
those of the individual. Although such ideas have been resisted within biology for some decades, only



                                                                                                           067
recently making a comeback (Wilson, D.S. & Sober, E., 1994), they do prove to be valid from both
computing and complexity science perspectives, e.g. (Sloman, A. & Chrisley, R., 2003).

For this to prove useful however there must be a possibility of a dynamic from the agents to the ‘end result’,
a way of searching current ‘state space’ (Lucas, C., 2002a), and expanding it in ways that enables such new
functionality. Yet this aspect of emergence is very much under researched so far, we have very little idea as
yet as to how we can arrange systems such that anything predictable will emerge, let alone to achieve what
we, ideally, would wish to see. This is perhaps the greatest challenge to be met in the future by the
complexity science community. But given this limitation, can the ideas we have outlined contribute already
in any way to current robotic research ? We shall see that they can and they do.


Enter the Robot
An implied embryogenesis perspective provides some, highly scaleable, advantages for robot designers
(Bentley, P.J. & Kumar, S., 1999). These include ‘adaptability’, the ability to respond to context (Quick, T.
et al., 1999); ‘compactness’, the ability to code large structures in an efficient form; and ‘repetitiveness’, the
ability to reuse the same structures or subroutines for many different functions. By using these techniques,
in for example the evolutionary design of neural networks (Astor, J.C & Adami, C., 2000), we can evolve
functional robot controllers (Jacobi, N., 1995), which potentially can interface with humans (Kanada, Y. &
Hirokawa, M., 1994). Self-organization is also a useful biological technique which can be used for evolving
robotic functionality (Nolfi, F. & Floreano, D., 2000; Kim, D.H., 2004), thus by combining the two
perspectives (low-level agent development and higher level agent interactions) we may, advantageously,
enable an ‘embodied’ form of ‘autopoietic’ (Lucas, C., 2004a) emergence, a coevolution of situation and
actor. Note that we have two opposing drives here, the first (embryogenesis) expands state space, it add new
possibilities, new options or combinations to the mix, the second (self-organization) reduces this diversity, it
selects from those many options only those possibilities than can persist, the functionally stable states of the
system. It moves from the system’s starting points in state space to its ‘attractors’. But these should not be
viewed in isolation, they are only stable in terms of the current environment, if the context changes then that
stability can be lost and another, alternative, stable state must then be found. This is, in fact, what we mean
by learning or ‘epigenesis’, the move from one stable state in a certain context to another stable state in a
different context. If our robot cannot do this, if it fails to ‘adapt’, becoming what we term ‘fragile’, then it
has insufficient options (‘requisite variety’ in cybernetic terminology) to cope with the diversity of its
environmental perturbations.

Designing robots that can overcome this tendency to be highly domain restricted has been a major headache
in AI history, so how can our new perspective help? If we are to ‘grow’ some form of robot from scratch
then four aspects are necessary. Firstly we must have a part (or a set of different parts) which can increase in
number, secondly those parts must be able to associate (communicate and/or stick together) in some way
such that they can form aggregates (some equivalent to ‘cell-adhesion’ or ‘morphoregulatory’ molecules -
Edelman, G., 1992), thirdly they must have the freedom to self-organize, (i.e. to change their configurations
and communications dynamically), and finally we need to allow for environmental influences to be able to
trigger these reorganizations (allowing adaptability). To see the latter two aspects in a different light, self-
organization restricts (‘canalizes’) the possibilities open to the system, it is a form of internal selection. The
environment puts stress or bias on the system to achieve a viable function, causing it to escape poor
attractors and flip to better ones, it is a form of external selection (if our system can’t so adapt it simply
dies). But as we add new units to the mix, as we ‘grow’ the self-organizing system (Fritzke, B,. 1996),
perhaps creating a 3D ‘morphology’ e.g. (Eggenberger, P., 1997), then we both add to and reconfigure its
attractors, so that in this way we can increase the ‘requisite variety’ until our system can, in fact, cope with
the target environment. This is similar to the way in which we make additional synaptic neuronal
connections with learning, we increase the complexity of the system by creating additional ‘concepts’ or
ideas, new options or associations.

Incorporating these four aspects into a real robot system is however a very demanding task, and not one that
has yet been attempted. Firstly we cannot grow artificial parts, they do not ‘reproduce’ in any sense. The
best we can do is either to ‘simulate’ such systems (perhaps eventually implementing the end result, e.g.


068
Bentley, 2004), or to have our robot (somehow) pick up and incorporate extra parts that happen to be ‘lying
around’ in the environment. Secondly it is unclear how we should have the parts interact in a way suitable
for stimulating development, such that it can possibly start to self-organize itself. Thirdly we have the
problem of how to allow the environment to change the configuration, to ‘disturb’ the robot in some way, in
such a manner as to force it to re-design itself. Even if these three major obstacles are overcome, then we
still do not know how to use our parts to self-assemble ‘critters’ with specific functions - but of course we
still don’t know how nature does that either (Raff, R.A., 1996). This relates to understanding how the three
processes (phylogeny, ontogeny and epigenesis) interact, but once we can do this then we have the potential
to build what have been called ‘POEtic machines’ (Teuscher, C., 2001). For the future, perhaps all we can
say, is that “we live in interesting times...”


References
Ackley, D. H. and Littman, M. L. (1992). Interactions between learning and evolution. In Langton, C.,
     Farmer, J., Rasmussen, S. and Taylor, C. (Eds.), Artificial Life II: Proceedings Volume of Santa Fe
     Conference, Vol. XI. Addison Wesley.
Astor, J.C and Adami, C. (2000) A Developmental Model for the Evolution of Artificial Neural Networks.
Artificial Life 6: 189-218. http://www.krl.caltech.edu/~adami/Alife2000.pdf
Bak, P. (1996). How Nature Works: The Science of Self-Organized Criticality. Copernicus.
Bard, J. (1990). Morphogenesis. Cambridge University Press.
Bentley, P. J. (2004) Controlling Robots with Fractal Gene Regulatory Networks. In de Castro, L. & von
     Zuben, F. (Eds.), Recent Developments in Biologically Inspired Computing. Idea Group Inc.
http://www.cs.ucl.ac.uk/staff/P.Bentley/BECH5.pdf
Bentley, P. J. and Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies for an
     Evolutionary Design Problem. Genetic and Evolutionary Computation Conference (GECCO '99), July
     14-17, 1999, Orlando, Florida USA, pp.35-43. RN/99/2.
     http://www.cs.ucl.ac.uk/staff/P.Bentley/BEKUC1.pdf
Bonabeau, E., Dorigo, M. & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems,
     Oxford University Press.
Brooks, R.A. (1990). Elephants Don't Play Chess. Robotics and Autonomous Systems Vol. 6, pp. 3-15.
http://www.ai.mit.edu/people/brooks/papers/elephants.ps.Z
Campbell D.T. (1974). Downward causation in Hierarchically Organized Biological Systems. Studies in the
     Philosophy of Biology, Ayala F.J. & Dobzhansky, T. (Ed.). Macmillan Press.
Corning, Peter. (1995), Synergy and Self-organization in the Evolution of Complex Systems. Systems
     Research 12(2): 89-121. John Wiley & Sons Ltd.
Edelman, G. (1992). Bright Air, Brilliant Fire: On the Matter of the Mind. Basic Books.
Eggenberger, P. (1997). Evolving Morphologies of Simulated 3d Organisms Based on Differential Gene
     Expression. 1997. In Husbands, P. and Harvey, I. (Eds.), Proceedings of the 4th European Conference
     on Artificial Life (ECAL97). MIT Press. ftp://ftp.ifi.unizh.ch/pub/institute/ailab/techreports/97.20.ps.gz
Epstein, J.M. and Axtell, R. (1996). Growing Artificial Societies. Brookings Institution Press.
Fritzke, B. (1996). Growing Self-organizing Networks - Why?. In Verleysen, M. (Ed.), ESANN'96:
     European Symposium on Artificial Neural Networks, p. 61-72. D-Facto Publishers, Brussels.
http://pikas.inf.tu-dresden.de/~fritzke/ftppapers/fritzke.esann96.ps.gz
Futuyma, D.J. (1986). Evolutionary Biology, 2nd Edition. Sinauer Associates, Inc.
Heylighen, F. (1999). The Science of Self-Organization and Adaptivity. Encyclopedia of Life Support
     Systems.
http://pespmc1.vub.ac.be/Papers/EOLSS-Self-Organiz.pdf
Hillis, W.D. (1991). Co-Evolving Parasites Improve Simulated Ecolution as an Optimization Procedure. In
Langton, C., Farmer, J., Rasmussen, S., & Taylor, C. (Eds.), Artificial Life II: Proceedings Volume of Santa
     Fe Conference, Vol. XI. Addison Wesley.
Holland, J. (1992). Adaptation in Natural and Artificial Systems. MIT Press.
Holland, O. and Melhuish, C. (1999). Stigmergy, Self-Organization, and Sorting in Collective Robotics.
Artificial Life 5: 173–202. http://robotics.usc.edu/~maja/teaching/stigmergy.pdf
Jakobi, N. (1995). Harnessing Morphogenesis. International Conference on Information Processing in Cells
     and Tissues, Liverpool. ftp://ftp.informatics.sussex.ac.uk/pub/reports/csrp/csrp423.ps.Z


                                                                                                           069
Kanada, Y. and Hirokawa, M. (1994). Stochastic Problem Solving by Local Computation based on Self-
     organization Paradigm. IEEE 27th Hawaii International Conference on System Sciences, pp. 82-91.
http://www.rwcp.or.jp/people/yk/CCM/HICSS27/paper/CCM-ProblemSolving.html
Kauffman, S. (1993). The Origins of Order - Self-Organization and Selection in Evolution. Oxford
     University Press.
Kim, D.H. (2004). Self-Organization for Multi-Agent Groups. International Journal of Control, Automation,
     and Systems, vol. 2, no. 3, pp. 333-342, September 2004.
     http://ijcas.com/admin/paper/files/IJCAS_v2_n3_pp333-342.pdf
Lucas, C. (1999). Complexity Philosophy as a Computing Paradigm. Self-Organising Systems - Future
     Prospects for Computing Workshop. UMIST 28/29 October 1999.
     http://www.calresco.org/lucas/compute.htm
Lucas, C. (2000a). Genetic Algorithms - Nature's Way. CALResCo introduction. Online WWW.
http://www.calresco.org/genetic.htm
Lucas, C. (2000b). Perturbation & Transients - The Edge of Chaos. CALResCo introduction. Online
     WWW.
http://www.calresco.org/perturb.htm
Lucas, C. (2002a). Attractors Everywhere - Order from Chaos. CALResCo introduction. Online WWW.
     http://www.calresco.org/attract.htm
Lucas, C. (2002b). Boolean Networks - Dynamic Organisms. CALResCo introduction. Online WWW.
     http://www.calresco.org/boolean.htm
Lucas, C. (2004a). Autopoiesis and Coevolution. CALResCo introduction. Online WWW.
     http://www.calresco.org/lucas/auto.htm
Lucas, C. (2004b). Emergence and Evolution - Constraints on Form. CALResCo introduction. Online
     WWW. http://www.calresco.org/lucas/quantify.htm
Lucas, C. (2004c). Fitness and Synergy. CALResCo introduction. Online WWW.
     http://www.calresco.org/lucas/fitness.htm
Lucas, C. (Ed.). (2004d). The Self-Organizing Systems FAQ. Usenet comp.theory.self-org-sys. Online
     WWW. http://www.calresco.org/sos/sosfaq.htm
Nolfi, F. and Floreano, D. (2000). Evolutionary Robotics: The Biology, Intelligence, and Technology of
     Self-Organizing Machines. MIT Press.
Quick, T., Dautenhahn, K., Nehaniv, C.L. & Roberts, G. (1999). The Essence of Embodiment: A
     Framework for Understanding and Exploiting Structural Coupling Between System and Environment.
     Proc. Third International Conference on Computing Anticipatory Systems, Liège, Belgium.
     http://www.cs.ucl.ac.uk/staff/t.quick/papers/quick_casys99.ps.gz
Raff, R.A. (1996). The Shape of Life: Genes, Development and the Evolution of Animal Form. The
     University of Chicago Press.
Slack, J. M. (1991). From Egg to Embryo. Cambridge University Press.
Sloman A and Chrisley R. Virtual Machines and Consciousness. Journal of Consciousness Studies, 10, No.
     4-5, 2003. http://www.cs.bham.ac.uk/research/cogaff/sloman-chrisley-jcs03.pdf
Teuscher, C. (2001). On the State of the Art of POEtic Machines. Technical Report 01/375, Swiss Federal
     Institute of Technology.
     http://www.teuscherresearch.ch/download/christof/papers/teuscher_techrep01.pdf
Ünsal, C. (1993). Self-Organisation in Large Populations of Mobile Robots. Masters Thesis. Virginia
     Polytechnic Institute and State University. http://armyant.ee.vt.edu/unsalWWW/cemsthesis.html
Wilson, D.S. and Sober, E. (1994). Reintroducing group selection to the human behavioral sciences.
     Behavioral and Brain Sciences 17 (4): 585-654.
     http://www.bbsonline.org/documents/a/00/00/04/60/bbs00000460-00/bbs.wilson.html
Wolpert, L. (1998). Principles of Development. Oxford University Press.




070

								
To top