Evolving Vision
in an
ALife world .
Preliminary Report 20th December 2002.
By Robert Cox and Peter Main
© 2002
University of Canberra
School of Computer Science.
ALifeVision1_rep1_3.doc
1. Abstract
A new artificial life world uses cooperative artificial neural networks to
successfully evolve a simulated adaptive vision/action system. ALifeVision
(“ALV”) was designed to explore aspects of perception/action systems in
simulated creatures and provides a platform to explore software models of
cooperative specialist neural circuits in nature.
The program is intended to eventually assist ANN ensemble development for
incompletely understood information domains. The dual perceptual-vision/actioncontrol design should enable further study of the evolutionary interaction between
acquired and instinctive behaviours, such as claimed for the Baldwin effect.
The working model world evolves a primitive, fully instinctive, cooperative vision–
action control system. A series of simple, comparative perceptual-response testing
patterns were used to confirm visual perception useful to the artificial organisms.
A demonstration program is available for interested parties to verify the simulated
vision system.
2. Introduction
A simple artificial life software simulation world has been developed at the
University of Canberra to explore evolution of Artificial Neural Network (“ANN”)
ensembles applied to a vision-action system.
The vision-action system was chosen since a wide variety of comparative anatomies
exist in evolution for the eye: each evolving to suit the needs of the owner and its
adaptive pressures. Artificial neural networks were chosen to simulate behaviours,
since they lend themselves both to instinctive (inherited ) and learnt (experiential)
behaviours. Taken together, this provides a rich area to explore aspects of useful
vision-action systems akin to the real world, consistent with Calebretta’s 1998
recognition of a need for integration. [ Attachment ]
ANN’s have been widely researched from the early 1990s, however there remains
uncertainty as to when such methods are reliably successful. They appear to have a
place in complex domains with conflicting demands and this matches the
compromises evident in natural evolution. Exploring correlations between biology
and interactive ANN’s and eventually provide object-oriented services for software
applications, is the longer term aim of this project.. ALife simulations may further
elicit aspects the interaction of acquired and instinctive evolved behaviours. [Turney]
To this purpose, we have constructed an ALife world that evolves a primitive ANN
cooperative vision–control system. A series of simple, comparative perceptualresponse testing patterns were used to confirm the evolved adaptive fitness.
Work is now in progress to explore the additional effects of individual learnt
behaviours on the above successful model.
At an abstract level, functioning ANN ensembles may provide dynamic modelling
resources when dealing with incompletely characterised dynamic populations.
[Hannon & Ruth]. Artificial life simulations also have an enduring attraction to
mirror aspects of biological ecosystems that appear based on complex evolvable
feedback systems. [Ward ]
We have initially used a stabilised communications channel between differing coevolved cooperative ANN structures. [ fig 1] . This version of the program uses
fully instinctive behaviours to establish an initial baseline for further work. By
combining both Alife evolution and ANN ensembles/cooperatives, solutions to a
wider range of problems may be easier to build, after evolution develops a fit
population of ANNs for a problem domain.
Recent progress in artificial vision systems intended for implantation, suggest that
an understanding of the communication between artificial implants and an existing
individual may remain crucial to success. Neural plasticity of the host organism,
feedback control of implants, and the nature of information encoding itself may
become new limiting issues. Retinal implants that substantially process visual
information and present this to the host optic brain, may still depend on the adaptive
capacity of the host to finally integrate and use the information presented. [ Boahen,
K. 1996 Retinomorphic Vision systems II: communication channel design]
In medical research , direct stimulation of “phosphemes” in the visual cortex has
achieved limited success and Boahen’s sophisticated designs appear a promising next
step. Proof of use by the brain must await the results of first successful implants.
Machine-vision’s real success may finally be determined by the capacity of two
adaptive neural networks, one silicon and one biological, with or without
bidirectional communications, to learn cooperative communication. The
requirements for successful evolution and adaptation between ANN specialist nodes
may thus be crucial to useful artificial sensory/action systems.
Successfully developing, using and modelling such cooperation with a simple
vision/action example, is a central purpose of our ALifeVision project.
3. Hypotheses explored.
We have initially explored the hypotheses that:
1. A primitive use of simulated vision can evolve using ALife evolution of
cooperative neural networks, linked by a stabilised, non-evolving
communication channel;
2. ALife evolved vision adaptively fits “seeing” individuals to an example ALife
world with beneficial and harmful objects;
3. Either “blind” or “hungerless” populations can evolve to adaptively survive
in the same ALife world;
4. An evolved population of individuals using both “vision” and “hunger” to
decide on actions, is adaptively superior to populations using either alone.
4. Methods
A Windows-based PC platform was chosen with Delphi as the programing tool, for
ease of development and visual object programming. Preliminary versions were
explored with one “world” and a “visual brain” (VB) cooperating with an “action
brain” (AB), via a commonly coded decision protocol. Evolutionary stability of the
world was explored extensively with different parameters.
The world operation conceptually was derived from cellular automata, with the
capacity to evolve adaptive behaviours arising from ANN weights, and the absence
of inter-cell communications. A key requirement was to demand cooperation
between two specialist ANN-“nodes” and thus a dual network interaction was the
minimum requirement to explore this.
Our reason for this was the observed lack of scalability of ANN’s, and the
observation that nature provides CNS “centres” or peripheral nervous system
“ganglia” to process a specialist systems information, before exchanging outputs with
the rest of the organisms nervous system. [ Endnote ]
Thus to provide interactive specialist subsystems that can evolve in cooperation, the
nature of the communication between them requires understanding. We have
initially proceeded with only one-way communication, to see whether useful
behaviours can evolve. Feedback propagation from the central neural decision
processing to the sensory network, is a feature of many modulated sensory systems,
however we required a simplistic initial model to test the concepts.
We did not allow the communications process itself between the cooperative ANNs to
evolve, instead choosing to set this as a feed-through output, that would allow ready
testing of the contribution of the two specialist ANNs (AB and VB), to each
creature’s overall testable behaviour.
The “creatures” being simulated were located within one “cell” of the world, and were
assumed a capacity to “see” the 8 cells around them, plus their own cell, in a
cartesian X-Y system. [Endnote: assumptions] Randomisation, and initial
bootstrap values were explored to achieve an initial survival population suited to
reasonable evolutionary generations with limited processing time (100 to 150
generations were targeted to look for testable evolved behaviours).
The VisionBrain could evolve a simple action decision output, with the ActionBrain
then modifying the recommendation from VB according to the current state of the
creatures “health” (represented as “hunger-drive”)
The cells contained nil, “edible plant”, “poison plant”, “rock”, or another creature,
with world “cycle” processing rules that excluded creature to remain co-located with
a rock. The borders of the world were processed as if all cells outside the world
boundary contained rock [.
There is feed-forward from VB to AB but no feedback from AB to VB in the
successfully evolved first version of perceptual simulated vision.
ALifeVision model 1.
World
containing
many
creatures
Minimum
“Eye”
with
9 cells
Cell
contents=
Edible Plant
Poison Plnt
Animal
Rock
Empty
Fig 1.
Visio
n
Actio
n
Brain
Brain
Actions
=
eat
sleep
left
right
up
down
Creature’s
internal
state
(“ Health”)
Split population mutation and mating routines were used during development to
confirm aspects of the VB and AB behaviours developed. Modulated random
behavioural decisions were implemented during development, but removed in the
final success-tested version. [ results & Appendix]. Both VB and AB could be
switched on and off during evolution and testing, with separate populations capable of
swapping AB and VB sets to compare resultant overall behaviour.
Competitive drive to extinction of two populations subject to different mate and/or
mutate algorithms and rates per generation, were implemented during development,
but not included in the final demonstration version.
The evolving populations were finally tested for all hunger-drive conditions, to
derive statistical descriptors of the testable behaviours. A progressive shift of adaptive
behaviours over the world’s epoch was taken to confirm successfully evolved
change.
Combinations of the evolved ANN capacities at different stages of simulated
evolution were systematically observed, and logged for later Excel analysis.
Experiments were controlled for multiple aspects of the World, and we
performed extensive crossover trials of populations in competition to confirm the
competitive value of both sighted blind behaviour in the world. This included
behaviour in response to perceived surroundings, combined behaviour of both
vision and hunger modalities, and responses to hunger alone. [ Results 1]
Fig 2. Sensitivity to world conditions in preliminary version.
Fig 3. Control showing neutral competing population behaviour/ upwards bias.
5. Results
We substantially confirmed the first 3 of our 4 hypotheses, and obtained sufficient
data to suggest that ecosystem features will determine the overall fitness of an
example organism in their environment. This is consistent with concepts of
cost/benefit ratios being central to the use an organism can make of a given adaptive
capacity in a given environment. A good use must be made of a sensory-action
system for it to be beneficial to a species [****see: blind tiger snake survival]
Early tests revealed that the AB alone could rapidly evolve behaviours to optimise
survival in the world without VB input. Crossover tests also showed that VB
evolution could adapt successfully alone without AB decision-modification.
Modulated random behavioural decisions were used during development, but
removed in the final version due to an incapacity to completely evolve out deadend behaviours arising from the initial random inputs.
Mutation rates, and world cycle poison-plant replacement and edible plant restocking,
all required heuristic adjustment to obtain initial parameters to survive an early
extinction “pinch” when AB and VB had minimal retained mutations.
Work to date suggests that combined evolution of a perception system cooperating
with a response control system, is a promising method to build adaptive ANNs from
two different designs. There are difficulties with the sequence and manner of
adaptive pressures being applied to achieve a coordinated system, as the resultant
organism can readily adapt to a maladaptive niche. There is however, clear
experimental evidence of adaptive benefit to the decision ANNs, of an evolving
visual ANN providing perceptual information.
The manner of “mating” and “mutation rates” appear crucial as to whether adaptation
or mal-adaptation occurs in given circumstances, as does the rate of mutation per
generation. “Chaotic” type effects appear significant at times from the initial status of
the simulated world. [ ** See: Alife9_results_Sun5b.doc]
The following comprehensive statistical analysis results from testing all the surviving
population, with all possible values of hunger, for selected perceptual challenges in a
9-cell test-bed.
In the following tables, the abbreviations are as follows: OnPP : on
poison-plant; onEP : on edible plant; lftPP poison-plant to left; lftEP edible plant
to left; lftRK : rock to left; lftAN : animal to left.
5A. Shortly after bootstrap: there is virtually no perceptive discrimination
between EP and PP (note that the VBrain supplies a feed-through action command
that can decide if ABrain is switched off):
ABRain On
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 12.33% 12.33% 12.33% 12.33% 12.33% 12.33%
Eat
50.97% 51.41% 51.41% 51.41% 51.41% 51.41%
Up
07.26% 07.26% 07.26% 07.26% 07.26% 07.26%
Right 07.90% 07.90% 07.90% 07.90% 07.90% 07.90%
Down 07.04% 07.04% 07.04% 07.04% 07.04% 07.04%
Left 14.51% 14.07% 14.07% 14.07% 14.07% 14.07%
ABRain Off
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 73.68% 73.68% 73.68% 73.68% 73.68% 73.68%
Eat
05.26% 05.26% 05.26% 05.26% 05.26% 05.26%
Up
00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Right 05.26% 05.26% 05.26% 05.26% 05.26% 05.26%
Down 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Left 15.79% 15.79% 15.79% 15.79% 15.79% 15.79%
5B. The Vbrain appears to develop a compensatory bias to neutralise ABrain’s
leftwards bias [1]. AB/VB combination also shows discriminatory perceptual vision
between poison plant and edible plant [2]
ABRain On turn 86671
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Eat
00.01% 51.39% 00.39% 51.39% 00.39% 51.39% [2]
Up
09.55% 09.81% 09.66% 09.81% 09.66% 09.81%
Right 68.92% 14.15% 69.53% 14.15% 69.53% 14.15%
Down 12.56% 14.95% 12.34% 14.95% 12.34% 14.95%
Left 08.96% 09.70% 08.08% 09.70% 08.08% 09.70%
ABRain Off
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Eat
00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Up
00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Right 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% [1]
Down 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Left 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
5C. Vbrain develops some independantly useful behaviours:
EP to PP differentiation [3] and minor Left animal and Left Edible plant attraction
[4]; poison plant avoidance [5]
ABRain On
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 03.78% 09.27% 09.74% 09.07% 09.74% 09.07%
Eat 00.36% 59.77% 04.89% 40.68% 04.89% 40.68%
Up 10.47% 08.47% 07.69% 10.41% 07.69% 10.41%
Right 25.91% 02.21% 57.98% 16.61% 57.98% 16.61%
Down 47.17% 09.59% 07.41% 07.05% 07.41% 07.05%
Left 12.31% 10.69% 12.30% 16.17% 12.30% 16.17%
ABRain Off
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Eat 00.00% 100.00% 07.69% 67.83% 07.69% 67.83% [3] [4]
Up 60.84% 00.00% 10.49% 30.07% 10.49% 30.07% [5]
Right 39.16% 00.00% 81.82% 02.10% 81.82% 02.10% [5]
Down 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Left 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
5D. After extended run some perverse behaviours: 2.442 million turns
generation 3278. Poison plants are avoided but sometimes eaten with ABrain on [6]
This is consistent with “niche construction”.
ABRain On
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Eat
24.46% 53.52% 08.52% 52.85% 08.52% 52.85% [6]
Up
42.27% 01.35% 38.10% 02.38% 38.10% 02.38%
Right 22.21% 00.00% 39.54% 00.09% 39.54% 00.09%
Down 06.22% 00.26% 04.32% 00.29% 04.32% 00.29%
Left 04.84% 44.88% 09.51% 44.39% 09.51% 44.39%
ABRain Off
test>> onPP onEP lftPP lftEP lftRK lftAN
Sleep 00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Eat
00.00% 00.00% 00.00% 00.00% 00.00% 00.00%
Up
36.25% 00.00% 82.50% 01.25% 82.50% 01.25%
Right 01.25% 00.00% 00.00% 00.00% 00.00% 00.00%
Down 62.50% 12.50% 06.25% 11.25% 06.25% 11.25%
Left 00.00% 87.50% 11.25% 87.50% 11.25% 87.50%
moveIQ
800
600
eatIQ
400
mateIQ
200
0
-200
genIQ
0
100
200
300
400
500
600
-400
Fig 4. Sample EatIQ/ MoveIQ response to change in edible plant availability
moveIQ
400
300
eatIQ
200
mateIQ
100
0
-100
genIQ
0
50
100
150
200
-200
Fig 5. A sample Response to Rock density, then reduced edible plant.
5. Discussion.
Hypotheses outcomes: Our results provide simulation evidence that within the
ALifeVision world, vision-action cooperatives :
1. can evolve using ALife cooperative neural networks, linked by a stabilised
communication channel;
2. adaptively fits “seeing” individuals to an ALife world with beneficial and
harmful objects.
3. allows either “blind” or “hungerless” populations to evolve and adaptively
survive in the same ALife world.
4. Finally , we found that an evolved population of individuals using both
“vision” and “hunger” to decide on actions, is not always adaptively superior to
populations using either alone, dependant on the qualities of the “world” . This
appears to be a complex interactive issue, probably depending on 1) the nature
of the “benchmarks” for fitness being used; 2) the competing “ecology” of the
world in which fitness is tested; 3) the length of time available for evolution, and
4) the complexity of world challenges providing major selective pressures (to list
but a few candidates).
Overall, our results demonstrate an example simulation that has a capacity to
develop constrained-communications cooperatives between differing neural
network designs as a population of “creatures”, each of which demonstrate adaptive
fitness to the world domain. The perceptual use made of the AB-system, is to modify
the simple recommendations of the VB system for a decision, with the current level of
hunger-drive. This represents a merging of two separate functions arising from both
the internal creatures state, and perception of the adjacent world cells.
This model with further development, should enable the exploration of other aspects
of simple cooperative specialist systems, including simple multicellular creatures.
Because of the capacity to selectively disable different components, and separately
control mutations, evolutions and the value of decisions, modelling of perceptual
loss, perceptual processing loss, perceptual-to-AB lesions, can all occur. This
provides a basis to explore the effects of selective “dementia” on the ANNs.
The effect of “dementing” processes such as excess mutation rates with age may thus
be modelled as a population behaviour.
As Turney states in his position paper, Lamarckism applied to real-world problems
requires a mapping from phenotype to genotype, however this applies only where
genotype descriptors are used to facilitate the evolution itself. Our initial program
has dispensed with genetic descriptors of the structures and evolved the network
weights directly, and thus so long as the network design of instinctive versus learnt
behavioural ANNs are the same, we can see no theoretical reason why the two cannot
be interchangeable.
A number of biological models transmit acquired behaviours to subsequent
generations, via non-genetic means [ endnote]. In so doing, the net effect of a
functioning Baldwin effect may be to deliver a Lamarckian effect via cumulative
Darwinian evolution, so the result becomes a question of mechanism and time: the
end result is similar [endnote] .
Work to test these issues is in progress, since the evolvable instinctive vision
framework we have, should allow a comparative testing of both direct Lamarckian
and Baldwin effects in competition [see: Endnote/ assumptions] .
6. Conclusions:
ALifeVision provides a platform that allows the exploration of an evolving
cooperative ANN vision-action system useful to the artificial organisms. Further
directions of work intended are the requisite features of communication between
different instinctive systems, with development of simplified individual plasticity (ie
experiential learning in addition to instinctive behaviour), communication,
specialization and education of the populations.
Further automated testing under diverse conditions is required to fully
characterise the relationships between sensory competence and benefit/harm of
the world features.
The relationship between the nature of the survival challenges and the benefit of
types of vision-action processing evolved, invites further exploration. Aspects of
our results may be applicable generally to the interaction between different
forms of sensory systems and diverse neural processing capacities.
7. Demonstration Program.
A simplified version of AlifeVision is available that provides a capacity to confirm
many of our results to date. [ www.uc.edu.au ] The program runs under Windows
9xx up platforms and requires no specific installation. Evolved sample creatures can
be saved, reloaded, and re-populate saved worlds to re-run populations in different
conditions. A limited capacity to change the world setup is provided, and ANN test
reporting and logging can confirm evolved behaviours.
----------------===================---------------8. ENDNOTES
a) Background Co-operative specialist effects from Biology
Some tissues that are grown embryonically and in youth for interconnected,
cooperative functions, appear to become stabilised in later life and are not normally
replaceable. Thus central nervous system and cardiac muscle, may require growth
and maturation with cooperative experience to work properly, because of a
demand for the persistence of stable, communicating collections of specialist cells.
This loss of the “plasticity” feature in some tissues of multicellular organisms ,
suggests that there may be stability requirements at the foundation of specialist
neural network cooperatives, that are pre-requisites to competence. ANN software
analogues of such biological “co-dependancy” between specialist sub-systems, are
hoped to be explored via developments from this project.
Of the three typical biological neural circuits, our initial model is confined to that of
convergent circuits, from sensory input processing to simple action outputs.
Divergent neural circuits are planned for later multi-cellular clustering into functional
groups for communication . [Tobin & Morel].
The modelling of neural reverberating circuits appear applicable to the addition of
pattern memories to the current system. [Campbell. p.1010]
b) Bias and experimental design and proof of neutrality.
Program bias can be selectively examined, using differential behavioural controls on
competing populations. Such a process enables automated control of the effects of one
variable, with an experiment/control crossover design to proceed . Eventually, initial
bias arose via selection, since this provided a small population confined to a world
corner, where they could survive long enough to successfully mutate.
Some experiments based on a crossover experiment/control method of population
switching, confirmed the independent usefulness of both the AB and VB in isolation,
and the superiority to either of the combination.
Delphi 3 & 4 have incomplete array runtime out-of-range error detection and
reporting, and thus some simple array referencing errors led to bizarre unrelated
errors, some of which only appeared under certain evolutionary & population
condions. This type of problem reminds us that adding new code to functional code
renders it all “untested”in an overall neutral-bias sense, and this can be an especially
difficult problem in evolutionary platforms without crossover experiment/control
designs. Thus to prove the absence of experimental bias in a simulation , the
“world” itself needs to be subject to sufficient crossover tests to establish its fitness to
proceed experimentally. This requires many hours simulation runtime to verify each
version of code stability, before conclusions can be drawn.
c) Further work in progress.
Further work in planned and in progress includes:
 A minor alteration to allow switchable sphere or boundary world map
profoundly alters the evolutionary behaviour of populations. This has not
been fully tested to date.
 Script control
 Instinctive vs aquired visual perception/action balances
 Cost/benefit determination of instinctive vs aquired neural network
behaviours.
--------------------------==========================----------------------------9. Terminology
ANN : Artificial Neural Network
ANN ensemble : a collection of identically structured ANNs with different weights,
contributing to a net decision arbiter for a final process outcome;
ANN cooperative : two or more ANNs of different architecture, cooperatively
providing the processed output
ALife: Artificial Life simulated on a computer.
Baldwin effect : a cluster of observations reported by Baldwin (1896),
Morgan(1896), and Osborn (1896) wherein an adaptive capacity to learn behaviours
acquired during individual organisms lifetimes, facilitates the evolutionary
accumulation of similar instinctive behaviours, that may eventually replace the
learned behaviours.
Lamarckism : a theory proposed by Lamarck (in contradiction of Darwin’s evolution
by selection for fitness) where new genetic adaptations were somehow initiated by a
learned adaptation within an individual’s lifetime, and thence transmitted genetically
to all future generations.
---------------- ==========================================-------------10. References
Baldwin, J.M. 1896 A New Factor in Evolution reprint in: Adaptive Individuals in
evolving Populations: Models and Algorithms, edited by R K Belew and M Mitchell
(SFI studies in the Sciences of complexity, Proc. Vol. XXVI, Addison-Wesley,
Reading MA , 1996)
Boahen, K. [1996] Retinomorphic Vision systems 1: Pixel design.
www.pcmp.caltech.edu/anaprose/buster/ [retrieved 25 Nov 2002]
Boahen, K. [1996] Retinomorphic Vision systems II: communication channel design
www.pcmp.caltech.edu/anaprose/buster/ [retrieved 25 Nov 2002]
Calabretta et al., 1998 Modularity - Understanding the Development and
Evolution of Complex Natural Systems, Altenberg Workshops in Theoretical
Biology, Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg
(Austria), October 26-29, 2000.
http://www.kli.ac.at/workshops-c.html?stuff/workshops/wtb-00 [ 25 Nov 2002]
Campbell, N.A., 1996 Biology 4thEd. Benjamin/Cummings Publishing USA.
Dobelle, W. in : Second sight , New Scientist . 23 November 2002 p 34. GrahamRowe, D. Reed Business Information , Sydney Australia.
Hannon B. & Ruth M. 1994. Dynamic Modelling. Springer-Verlag USA
Tobin, A.J., Morel, R..E. 1997 Asking about cells. Saunders college Publishing .
USA.
Turney , P; ? 2000 Myths and Legends of the Baldwin Effect. Institute for
Infomation Technology National Research Council Canada Ottawa, Ontario, Canada.
Ward, M. 2000. Virtual Organisms. Thomas Dunne Books, USA.
11. Appendix.
a) from
http://www.kli.ac.at/workshops-c.html?stuff/workshops/wtb-00
from the site ? below to cross check
Modularity - Understanding the Development and Evolution of Complex Natural
Systems, Altenberg Workshops in Theoretical Biology, Konrad Lorenz Institute for
Evolution and Cognition Research, Altenberg (Austria), October 26-29, 2000.
Modularity cooperation
"The existence of modules is recognized at all levels of the biological hierarchy. In
order to understand what modules are, why and how they emerge and how they
change, it would be necessary to start a joint effort by researchers in different
disciplines (evolutionary and developmental biology, comparative anatomy,
physiology, neuro- and cognitive science). This is made difficult by disciplinary
specialization. [...] we claim that, because of the strong similarities in the intellectual
agenda of artificial life and evolutionary biology and of their common grounding in
Darwinian evolutionary theory, a close interaction between the two fields could easily
take place. Moreover, by considering that artificial neural networks draw an
inspiration from neuro- and cognitive science, an artificial life approach to the
problem could theoretically enlarge the field of investigation." (Calabretta et al.,
1998)
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