Content uploaded by Gordana Dodig Crnkovic
Author content
All content in this area was uploaded by Gordana Dodig Crnkovic on Jun 06, 2016
Content may be subject to copyright.
Info-computationalism and Morphological Com-
puting of Informational Structure
Gordana Dodig Crnkovic
Mälardalen University, School of Innovation, Design and Engineering,
Sweden, gordana.dodig-crnkovic@mdh.se
Abstract. Within the framework of info-computationalism, morphological
computation is described as fundamental principle for all natural computa-
tion (information processing).
Foundations of a New Science of Computation
Present computational machinery evolved from mechanical calculators to
electronic machines with vacuum tubes and then transistors, and to inte-
grated circuits and eventually microprocessors. During this remarkable de-
velopment of hardware towards ever smaller, faster and cheaper devices,
the computational principles remained unchanged: an isolated machine
calculating a function, executing an algorithm. Such machines were ade-
quately represented by the Turing Machine model. However, computation-
al machinery gradually started to change its character from isolated calcu-
lators to networked communicating devices. In the 1970s first networks
were created with computers linked together via telecommunications. The
emergence of networking involved a changed nature of computers and
computing as operating systems and applications started to access and use
the resources of each other, exchanging information.
Turing Machine model is sequential. As long as parallel processing, such
as occurring in networks, is synchronous, it can be sequentialized, and thus
Turing Machine model can be applied. However for networks with asyn-
chronous processes Turing Machine is not appropriate. As (Sloman 1996)
points out, concurrent and synchronized machines are equivalent to se-
quential machines, but some concurrent machines are asynchronous. (Do-
dig Crnkovic 2011)
Author's manuscript
Chapter in the book Integral Biomathics
Published by Springer
http://link.springer.com/chapter/10.1007/978-3-642-28111-2_10
pp 97-104
2
One of the main arguments in favor of universal computing is the often re-
peated claim in Computer Science (based on Turing machine model of
computation) that it is invariant on the details of implementation (hard-
ware). Computational complexity classes, themselves based on Turing
model of computation, are supposed to be substrate-independent general
abstractions. However, it turned out that Turing Machine model depends
essentially on the underlying assumption of classical physics:
The Turing machine is entirely classical, and does not allow for the possi-
bility that paper might have different symbols written on it in different un-
iverses, and that those might interfere with one another. (Deutsch 1997)
This fascinating insight in the fundaments of computing leads us directly
to the nascent field of Natural Computing, which sometimes is called Un-
conventional Computing or Physical Computing.
Natural Computation
According to the Handbook of Natural Computing (Rozenberg et al. 2011)
Natural Computing is “the field of research that investigates both human-
designed computing inspired by nature and computing taking place in na-
ture.” In particular, the book addresses:
Computational models inspired by the natural systems such as neural
computation, evolutionary computation, cellular automata, swarm intelli-
gence, artificial immune systems, artificial life systems, membrane compu-
ting and amorphous computing.
Computation performed by natural materials such as bioware in molecular
computing or quantum-mechanical systems in case of quantum computing.
Study of computational nature of processes taking place in (living) nature,
such as: self-assembly, developmental processes, biochemical reactions,
brain processes, bionetworks and cellular processes.
Especially important in the context of Natural Computing is that know-
ledge is generated bi-directionally, through the interaction between com-
puter science and the natural sciences. While the natural sciences are ra-
3
pidly absorbing ideas, tools and methodologies of information processing,
computer science is broadening the notion of computation, recognizing in-
formation processing found in nature as (natural) computation. (Rozenberg
and Kari 2008) (Stepney et al. 2005) (Stepney et al. 2006)
This new concept of computation allows for nondeterministic complex
computational systems with self-* properties. Here self-* stands for self-
organization, self-configuration, self-optimization, self-healing, self-
protection, self-explanation, and self(context)-awareness. Dodig Crnkovic
in (Dodig Crnkovic and Müller 2009) argues that natural computation (un-
derstood as processes acting on informational structures) provides a basis
within info-computational framework for a unified understanding of phe-
nomena of embodied cognition, intelligence and knowledge generation.
While computing nature is an old idea, dating back to Zuse, and developed
by number of other researchers (Fredkin, Wolfram, Chaitin, Lloyd) who
argue that all of the physical world computes, the question may be asked:
on what substrate does this computation goes on? Within the info-
computational framework, the answer is: information. All computational
processes in the Nature take place on informational structures (protoinfor-
mation).
Universe as Informational Structure
Von Baeyer (2003) suggests that information is to replace matter/energy as
the primary constitutive principle of the universe. Wolfram supports the
equivalence between the two descriptions:
Matter is merely our way of representing to ourselves things that are in
fact some pattern of information, but we can also say that matter is the
primary thing and that information is our representation of that. (Wolfram
in Zenil 2011, p. 389).
The universe is "nothing but processes in structural patterns all the way
down" (Ladyman, et al. 2007) p. 228. Understanding patterns as informa-
tion, one may infer that information is a fundamental ontological category.
What we know about the universe is what we get from sciences, as "spe-
cial sciences track real patterns" (p. 242). Thus the realism of this approach
4
is based on the claim that "successful scientific practice warrants networks
of mappings as identified above between the formal and the material" (p.
121). The ontology is scale-relative, as we generate knowledge through in-
teractions with the world (Dodig Crnkovic 2008) on different levels of ab-
straction (organization).
Information may be considered the most fundamental physical structure, as
in Floridi’s Informational Structural Realism (Floridi 2008). It is in perma-
nent flow, in a process of transformation, as observed in physics. We know
the world as a result of interaction and exploration:
Structural objects (clusters of data as relational entities) work epistemo-
logically like constraining affordances: they allow or invite certain con-
structs (they are affordances for the information system that elaborates
them) and resist or impede some others (they are constraints for the same
system), depending on the interaction with, and the nature of, the informa-
tion system that processes them. (Floridi 2008).
Info-computational Universe
Info-computationalism (Dodig Crnkovic 2006, 2009) is a unifying ap-
proach that brings together Informationalism (Informational Structural
Realism) of Floridi (2008); Informational Realism of Sayre (1976) and
(Ladyman, et al. 2007) – the informational universe - with the Naturalist
Computationalism/ Pancomputationalism (Zuse, Fredkin, Wolfram, Chai-
tin, Lloyd) – the computing universe. Info-computationalist naturalism un-
derstands the dynamical interaction of informational structures as compu-
tational processes. (Dodig Crnkovic forthcoming 2011) It includes digital
and analogue, continuous and discrete as phenomena existing in the physi-
cal world on different levels of organization (Dodig Crnkovic and Müller
2009). Digital computing is a subset of a more general natural computing.
In what follows I will present the idea of morphological computation
which is, as much of natural computation, different from the execution of
an in advance given procedure in a deterministic mechanical way. The dif-
ference is in the computational mechanism based on natural physical ob-
jects as hardware which at the same time acts as software or a program
governing the behavior of a computational system. Physical laws govern
5
processes which cause dynamical development of a physical system. Or in
other words, computational processes are manifestation of physical laws.
The new structure (data structure, informational structure) produced by
computational processes is a new program in the next step of time devel-
opment. Interestingly, morphological computation is not one of the topics
of the Handbook of Natural Computing, even though the fundamental
principles of morphological computing are underlying all of natural
computing.
Morphological Computation
Recently, morphological computing emerged as a new idea in robotics,
(Pfeifer 2011), (Pfeifer and Iida 2005), (Pfeifer and Gomez 2009) (Paul
2004). It has conceptually very important generalizable consequences with
regard to info-computationalism.
From the beginning, based on the Cartesian tradition, robotics treated sepa-
rately the body (machine) and its control. However, successively it became
evident that embodiment itself is essential for cognition, intelligence and
generation of behavior. In a most profound sense, embodiment is vital be-
cause cognition results from the interaction of brain, body, and environ-
ment. (Pfeifer 2011)
From an evolutionary perspective it is clear that the environment presents a
physical source of biological body which through morphological computa-
tional processes leads to the establishment of morphogenesis (governing
short time scale formation of an organism) and on long time scales govern-
ing evolution of species. Nervous system and brain evolves gradually
through interactions (computational processes) of a living agent with the
environment as a result of information self-structuring (Dodig Crnkovic
2008).
The environment provides a variety of inputs, at the same time as it impos-
es constraints which limit the space of possibilities, driving the computa-
tion to specific trajectories. This relationship is called structural coupling
by (Maturana& Varela 1980) and described by (Quick and Dautenhahn
1999) as “non-destructive perturbations between a system and its environ-
6
ment, each having an effect on the dynamical trajectory of the other, and
this in turn effecting the generation of and responses to subsequent pertur-
bations.” (Clark 1997) p. 163 talks about "the presence of continuous mu-
tually modulatory influences linking brain, body and world."
In morphological computing modeling of the agents behavior (such as lo-
comotion and sensory-motor coordination) proceeds by abstracting the
principles via information self-structuring and sensory-motor coordination,
(Matsushita et al. 2005), (Lungarella et al. 2005) (Lungarella and Sporns
2005) (Pfeifer, Lungarella and Iida 2007). Brain control is decentralized
based on the sensory-motor coordination through the interaction with en-
vironment. Some of the examples of the use of morphological computation
(Pfeifer 2011) in robotics are: “Yokoi hand” which can grasp any shape,
acting through self-regulation; “Passive dynamic walker” – the brainless
robot who walks down the slope; for which the dynamics of the interaction
with the environment is used for self-stabilization and “Insect walking”
with no central control for leg-coordination but global communication
through interaction with the environment.
Morphological Computing as Information Self-Structuring
In morphological computation, generation of sensory stimulation is
achieved by the interaction with the environment through constraints im-
posed by the morphology and materials. Through this interaction with the
environment, generation of correlations in sensors (self-structuring of sen-
sory data) is achieved by physical process. The induction of correlations
leads to reduction of complexity. Interaction occurs across multiple time
scales between body and control structure of an agent, and its environment.
According to (Lungarella et al. 2005) “sensory input and motor activity are
continuously and dynamically coupled with the surrounding environment.”
and “the ability of embodied agents to actively structure their sensory input
and to generate statistical regularities represents a major functional ratio-
nale for the dynamic coupling between sensory and motor systems. Statis-
tical regularities in the multimodal sensory data relayed to the brain are
critical for enabling appropriate developmental processes, perceptual ca-
tegorization, adaptation, and learning” (emphasis added). (Mirza et al.
2007) present an embodied, grounded individual sensorimotor interaction
7
history, based on information theoretic metric space of sensorimotor expe-
rience, dynamically constructed as the robot acts in the environment.
(Lungarella and Sporns 2005) give details of the study of the coupling and
interplay across multiple time scales between the brain, body, and envi-
ronment. Their findings are supported by the results of (Der 2011). It is
important to notice that structures emerge on all levels of control:
Embodied interactions impose statistical structure not only on “raw pix-
els” within primary sensory channels, but also (and perhaps more power-
fully so) on neural activity patterns far removed from the sensory peri-
phery. We predict that embodied systems operating in a highly coordinated
manner generate information and additional statistical regularities at all
hierarchical levels of their control architectures, including but not limited
to the immediate sensory input. (Lungarella and Sporns 2005)
The above mechanism provides the basis for the evolutionary understand-
ing of embodied cognition and knowledge generation. (Dodig Crnkovic
2008) In the process of self-organization of information, the states of the
distant parts of the system are synchronized by stigmergy - indirect coordi-
nation between agents or actions. The trace left in the environment by an
action increases the probability of the next action; so subsequent actions
reinforce and build on each other, resulting in a coherent behavior.
The results on self-organization of information and the development of
embodied cognition in living organisms have inspired the research pro-
gram in developmental robotics. Learning is a continuous and incremental
process and development proceeds through morphological change, growth
and maturation. Boundary conditions and physical limitations play an im-
portant role in the development of an agent, as they cause reduction of the
amount of information. Motor learning results in the reduction of space of
possible movements and enables acquisition of motor skills through explo-
ratory activity in the environment. It has been noticed that the greatest
learning occurs in childhood when the most vigorous growth occurs. (El-
man 1993) showed in training of networks to process complex sentences
that neural processing limitations appear advantageous as they contribute
to gradual learning. In a new born child initial low resolution vision suc-
cessively increases, and coarse control becomes gradually more fine-
grained (Pfeifer 2011) as learning proceeds. Only simple organisms are
8
born in their final form, while for complex organisms, development seems
necessary in order to successively achieve complexity, avoiding chaos.
Info-computational Character of Morpohogenetic Computing
Morphological computation makes visible essential connections between
an agent’s body, (nervous) control and its environment. Through the em-
bodied interaction with the environment, in particular through sensory-
motor coordination, information structure is induced in the sensory data,
thus facilitating perception, learning and categorization. The same prin-
ciples of morphological computing (physical computing) and data self-
organization apply to biology and robotics. Interesting to note is that in
1952 Alan Turing wrote a paper proposing a chemical model as the basis
of the development of biological patterns such as the spots and stripes on
animal skin, (Turing 1952). Turing morphogenesis did not originally claim
that physical system producing patterns actually performed computation.
Nevertheless, from the perspective of info-computationalism we can argue
that morphogenesis is a process of morphological computing. Physical
process – though not „computational“ in the traditional sense, presents
natural (unconventional), morphological computation. Essential element in
this process is the interplay between the informational structure and the
computational process - information self-structuring and information inte-
gration, both synchronic and diachronic, going on in different time and
space scales.
Morphology is the central idea in understanding of the connection between
computation (morphological/morphogenetical) and information. Materials
represent morphology on the lower level of organization – the arrange-
ments of molecular and atomic structures i.e., how protons, neutrons and
electrons are arranged on the level below.
Info-computational naturalism describes nature as informational structure
– a succession of levels of organization of information. Morphological
computing on that informational structure leads to new informational
structures via processes of self-organization of information. Evolution it-
self is a process of morphological computation on a long-term scale. It will
be instructive within the info-computational framework to study processes
9
of self organization of information in an agent (as well as in population of
agents) able to re-structure themselves through interactions with the envi-
ronment as a result of morphological (morphogenetic) computation.
References
Clark A. (1997). Being There: putting brain, body and world together again. Ox-
ford University Press.
Der R. (2011) Self-organization of robot behavior by self-structuring dynamical
information http://ailab.ifi.uzh.ch/brown-bag-lectures/self-organization-of-robot-
behavior-by-self-structuring-dynamical-information talk at Uni Zurich
Deutsch D. (1997). The Fabric of Reality. Penguin
Dodig Crnkovic, G. (2006) Investigations into Information Semantics and Ethics
of Computing, pp 1-133, Mälardalen University Press, Västerås, Sweden.
Dodig Crnkovic, G. (2008) Knowledge Generation as Natural Computation, Jour-
nal of Systemics, Cybernetics and Informatics, Vol 6, No 2
Dodig Crnkovic, G. (2009). Information and Computation Nets. Investigations in-
to Info-computational World. Information and Computation (pp. 1-96). Saar-
brucken: Vdm Verlag.
Dodig Crnkovic, G. (2011) Significance of Models of Computation from Turing
Model to Natural Computation, Minds and Machines,, Springer 21, 301-322.
Dodig Crnkovic, G. (forthcoming) Dynamics of Information as Natural Computa-
tion, Information, Selected Papers from FIS 2010 Beijing Conference,
http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing
Dodig-Crnkovic, G. and Müller V. (2009). A Dialogue Concerning Two World
Systems: Info-Computational vs. Mechanistic. In: Information and Computation,
World Scientific, Singapore 2011. Preprint available at:
http://arxiv.org/abs/0910.5001
Floridi L. (2008). A defense of informational structural realism. Synthese 161: 2,
Springer, pp. 219-253.
Ladyman, J., Ross, D., Spurrett, D., and Collier, J. (2007). Everything must go:
metaphysics naturalized. Clarendon Press, Oxford: pp 1-368.
Lungarella M. and Sporns O. (2005) Information Self-Structuring: Key Principle
for Learning and Development, Proceedings of 2005 4th IEEE Int. Conference on
Development and Learning, pp 25-30
Lungarella M., Pegors T., Bulwinkle D. and Sporns O. (2005) Methods for Quan-
tifying the Informational Structure of Sensory and Motor Data, Neuroinformatics
Volume 3, pp 243-262
10
Matsushita K., Lungarella M., Paul C., Yokoi H. (2005) Locomoting with Less
Computation but More Morphology, Proc. 2005 IEEE Int. Conf. on Robotics and
Automation, pp.:2008-2013
Maturana, H. R. & Varela, F. J. (1980). Autopoiesis and Cognition - The Realiza-
tion of the Living. Dordrecht, The Netherlands: D. Reidel Publishing.
Mirza N. A. et al. (2007) Grounded Sensorimotor Interaction Histories in an In-
formation Theoretic Metric Space for Robot Ontogeny, Adaptive Behavior, Vol
15(2): 167–187
Paul C. (2004) Morphology and Computation, Proceedings of the International
Conference on the Simulation of Adaptive Behaviour Los Angeles, CA, USA, pp
33–38
Pfeifer R. (2011) http://www.eucognition.org/index.php?page=tutorial-on-
embodiment Tutorial on embodiment
Pfeifer R. and Iida F. (2005) Morphological computation: Connecting body, brain
and environment. Japanese Scientific Monthly, Vol. 58, No. 2, 48–54
Pfeifer, R. and Gomez, G. (2009) Morphological computation - connecting brain,
body, and environment. In B. Sendhoff, O. Sporns, E. Körner, H. Ritter, & K.
Pfeifer, R., Lungarella, M. & Iida, F. (2007) Self-organization, embodiment, and
biologically inspired robotics, Science 318, 1088-1093.
Rozenberg G., Bäck T., Kok J. N., eds. (2011) Handbook of Natural Computing,
volume II. Springer. Forthcoming
Rozenberg, G. and Kari, L. (2008) ‘The many facets of natural computing’,
Communications of the ACM, 51. 72–83.
Sayre, K. M. (1976) Cybernetics and the Philosophy of Mind, Routledge & Kegan
Paul, London.
Stepney S., Braunstein S. L., Clark J. A., Tyrrell A., Adamatzky A., Smith R. E.,
Addis T., Johnson C., Timmis J., Welch P., Milner R., and Partridge D. (2005).
Journeys in non-classical computation I: A grand challenge for computing re-
search. Int. J. Parallel, Emergent and Distributed Systems, 20(1):5–19.
Stepney S., Braunstein S. L., Clark J. A., Tyrrell A., Adamatzky A., Smith R. E.,
Addis T., Johnson C., Timmis J., Welch P., Milner R., Partridge D. (2006) Jour-
neys in Non-Classical Computation II: Initial journeys and waypoints. Int. J. Pa-
rallel, Emergent and Distributed Systems. 21(2):97–125.
Turing A. M. (1952) Philosophical Transactions of the Royal Society of London.
Series B, Biological Sciences, Vol. 237, No. 641. (Aug. 14, 1952), pp. 37-72.
von Baeyer H.C. (2003) Information: The New Language of Science, Weidenfeld
and Nicolson
Zenil, H. (2011) Randomness Through Computation: Some Answers, More Ques-
tions., World Scientific Pub Co Inc, Singapore.
