UCNC 2013 Milan
Draft 2
Morphological Computing
and Physical Levels of Computation
Gordana Dodig-Crnkovic 1 and Mark Burgin 2
1 Mälardalen
University, Västerås, Sweden
gordana.dodig-crnkovic@mdh.se
2
Department of Mathematics, UCLA, Los Angeles, USA
mburgin@math.ucla.edu
Abstract
We expect future robots of various kinds, including nanorobots, to behave
intelligently in various degrees. Morphological computing emerged newly
as a programme in robotics for using physical properties of the robotic
body (including learning by information self-structuring) to automatically
produce behaviour. The idea is that morphology of an agent (living organism or a machine) constrains possible interactions within the body of the
agent, together with constraining agent’s interactions with the environment, as well as its possible development such as growth or reconfiguration. Natural computation in general can be seen as morphological computation on a variety of levels of organization of physical matter. It is a
computational model of the physical world that provides a basis for framing, parameter studies, optimizations and simulations of physical systems –
from nano-scale up to (cognitive) robots. The aim of this article is to elucidate the relationships between computation, information and morphology,
on different levels of organization of physical systems with relevance for
biology and robotics.
Computation. The Computing Nature
The Tuscan physicist, mathematician, astronomer, and philosopher Galileo
Galilei in his book The Assayer - Il Saggiatore (1623) declared that “the
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book of nature is written in the language of mathematics” and that the way
to understand nature is through mathematics. Today we understand nature
not only in terms of symbolic languages like mathematics but also through
computational physical representations such as simulations which provide
different (usually visual) rendering of natural phenomena in form of interactive models. Thus we can paraphrase Galileo by saying that the great
book of nature is written in the programming languages of natural computing. This challenges Kauffman’s view of nature without law, expressed in
the following:
“Even deeper than emergence and its challenge to reductionism in this
new scientific worldview is what I call breaking the Galilean spell. Galileo
rolled balls down incline planes and showed that the distance traveled varied as the square of the time elapsed. From this he obtained a universal
law of motion. Newton followed with his Principia, setting the stage for all
of modern science. With these triumphs, the Western world came to the
view that all that happens in the universe is governed by natural law. Indeed, this is the heart of reductionism. Another Nobel laureate physicist,
Murray Gell-Mann, has defined a natural law as a compressed description, available beforehand, of the regularities of a phenomenon. The Galilean spell that has driven so much science is the faith that all aspects of the
natural world can be described by such laws. Perhaps my most radical
scientific claim is that we can and must break the Galilean spell. Evolution
of the biosphere, human economic life, and human history are partially indescribable by natural law.”
In what follows we will argue that the new kind of lawfulness in the organization of the universe, and especially living systems rests on generative
laws, and not on a reduction of the whole to its constitutive parts. There is
a difference between classical reductionism which is a top-down approach
that definitely has huge importance in our understanding of the world answering the question what the world is made of – the elements of the construction. However, in order to understand complex systems organization
of the parts and interactions are crucial. That is where generative models
come in, top-down reversal of reductionist movie, collecting together pieces that make molecules, cells, organisms and other complex structures.
That is where Kauffman’s self-organization comes in. The basic claim is
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that nature compute by information processing going on in networks of its
constituent parts, hierarchically organized in layers. Structures selforganized on the short time scale through computational processes of morphogenesis and on the longer time scales as evolution. As all organisms
develop from a single cell and all animals have the same basic toolkit of
body-building genes, the development and evolution suggest common
generative processes. That is what we understand as computational processes and term morphogenesis. The underlying assumption is one of
computing nature – all processes in nature are understood as some kind of
computation (information processes) – natural computation. (Dodig Crnkovic and Giovagnoli, Computing Nature, forthcoming 2013)
The idea of computing universe has a long history and different flavors.
Computer scientist Konrad Zuse was the first to suggest (in 1967) that the
physical behavior of the entire universe is being computed on a fundamental level, possibly on cellular automata, by the universe itself which he
called The Computing Space/ Cosmos.
Subsequently emerging naturalist computationalism (pan- computationalism), developed among others by (Zuse, 1969) (Fredkin, 1992) (Wolfram,
2002), (Chaitin, 2007), (Lloyd, 2006) takes the universe to be a system that
constantly computes its own next state from the current one.
Hierarchy of Levels of Physical Computation
If the whole of nature computes, this computation happens on many different levels of organization of the physical matter. In Burgin_DodigCrnkovic_2011 three generality levels of computations are introduced,
from the most general to the most specific/particular one:
1.
2.
3.
Computation as any transformation of information and/or information representation. (This leads to natural computationalism in
its most general form)
Computation as a discrete transformation of information and/or
information representation. (This leads to natural computationalism in the Zuse and Wolfram form with discrete automata as a basis)
Computation as symbol manipulation. (This is Turing model of
computation and its equivalents).
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There are also spatial levels or scales of computations (Burgin and DodigCrnkovic, The Nature of Computation and The Development of Computational Models, submitted to CiE 2013):
1.
2.
3.
4.
5.
The macrolevel includes computations performed by current computational systems in global computational networks and physical
computations of macro-objects.
The microlevel includes computations performed by integrated circuits.
The nanolevel includes computations performed by fundamental
parts that are not bigger than a few nanometers.
The molecular level includes computations performed by molecules.
The quantum level includes computations performed by atoms and
subatomic particles.
Info-computationalism
Before we start to explain the relationships between form and its development we will introduce the framework of info-computationalism which
appears as well suited for this purpose. Info-computationalism is the view
combining informational structural realism (Floridi 2008, Sayre 1976,
Burgin 2012) and computationalism in the sense of computing nature
(Dodig Crnkovic 2011a, and references therein)
Information and computation are two interrelated and mutually defining
phenomena – there is no computation without information (computation
understood as information processing), and vice versa, there is no information without computation (information as a result of computational processes). (Dodig Crnkovic 2006 - 2012) Being interconnected, information
is studied as a structure, while computation presents a process on an informational structure. In order to learn about foundations of information,
we must also study computation. In (Dodig-Crnkovic, 2011a) the dynamics of information is defined in general as natural computation. With the
universe represented as a network of computational processes at different
scales or levels of granularity, information is both a substrate and a result
of natural computation (Dodig Crnkovic 2011). Computation is generally defined as information processing, see (Burgin, 2005)
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Morphological Computing in Nanoscale
According to natural computationalism/pancomputationalism [Dodig
Crnkovic 2006-2013] every physical system is computational, but there are
many different sorts of computations going on in nature seen as a network
of agents/actors exchanging ”messages”. The simplest agents communicate with simplest messages such as elementary particles (with 12 kinds of
matter and 12 kinds of anti-matter particles) exchanging 12 kinds of forcecommunicating particles. Example from physics that we can recast into actor model of computation is Yukawa’s theory of strong nuclear force modeled as exchange of mesons (as messages), which explained the interaction
between nucleons.
Complex agents/actors like humans communicate through languages
which use very complex messages for communication. Also, exchange of
information causes changes in actors. Those changes are simple in simple
actors such as elementary particle that can change its state (quantum numbers) while in complex agents with memory, communication results in
substantial changes in agents’ structures and their way of response.
"Mathematical structures commonly consist of objects connected by operations or relations. Sometimes the difference between these classes is
blurred, but in an interesting structure there are objects which accumulate
information expressive of their context in the structure. Sometimes this information can be `read' by the relations on the structure, which express a
formal `causality', whereby the distribution of information itself has a
structure. This appears to be a feature of our own universe."
Applied to a computational network as such mathematical structure it describes the computing nodes/agents that can internalize the outside network and thus change the way they interact as a consequence of previous
interactions. Nodes can be elementary particles or nano-systems exchanging molecules or animals in an eco-system exchanging complex messages
or any other interacting agents.
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Morphological Computing in Biology: Morphogenesis as Computation (Information Processing)
Morphology is a theory of the formative principles of a structure. (from the
Greek morphê - shape)
Morphogenesis is a study of the creation of shape during the development
of an organism. It includes one of the following four processes in the development: Patterning - the setting up of the positions of future events
across space at a variety of scales; Regulation of timing - the 'clock' mechanisms that regulate when events happen and Cell differentiation: changes
in a cell's set of expressed genes (its molecular phenotype).
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 did not originally claim that physical system producing patterns actually performs computation through morphogenesis. Nevertheless, from
the perspective of info-computationalism (Dodig Crnkovic 2009) we can
argue that morphogenesis is a process of morphological computing. Physical process – even though not „computational“ in the traditional sense, it
presents natural (unconventional), physical, morphological computation.
Essential element in this process is the interplay between the informational
structure and the computational process - information self-structuring (including information integration), both synchronic and diachronic, going on
in different time and space scales. Process of computation implements/executes physical laws which act on informational structures.
Through process of computation, structures change their forms.
All of computation on some level of abstraction is morphological computation – a form-changing/ form-generating process.
Computational view of autopoesis. According to Maturana and Varela
Autopoiesis (from Greek αὐτo- (auto-), meaning "self", and ποίησις (poiesis), meaning "creation, production") literally means "self-creation" and
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expresses a fundamental dialectic among structure, mechanism and function. The term was introduced in 1972 by Chilean biologists Humberto
Maturana and Francisco Varela:
“An autopoietic machine is a machine organized (defined as a unity) as a
network of processes of production (transformation and destruction) of
components which:
(i) through their interactions and transformations continuously regenerate
and realize the network of processes (relations) that produced them; and
(ii) constitute it (the machine) as a concrete unity in space in which they
(the components) exist by specifying the topological domain of its realization as such a network.” [1] p. 78
[1] Maturana, Humberto & Varela, Francisco ([1st edition 1973] 1980).
Autopoiesis and Cognition: the Realization of the Living. Robert S. Cohen
and Marx W. Wartofsky (Eds.), Boston Studies in the Philosophy of Science 42. Dordecht: D. Reidel Publishing Co.
“Though others have often used the term as a synonym for selforganization, Maturana himself stated he would "never use the notion of
self-organization, because it cannot be the case... it is impossible. That is,
if the organization of a thing changes, the thing changes".[3] Moreover, an
autopoietic system is autonomous and operationally closed, in the sense
that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a
dynamic of changes that can be recalled as sensory-motor coupling. This
continuous dynamic is considered as a rudimentary form of knowledge or
cognition and can be observed throughout life-forms.”
There is a difference between morphological computing and autopoiesis in
that autopoiesis presupposes closure of a system, while morphogenesis can
happen in non-living systems as well, such as crystals or hurricanes. Morphogenesis is a basis for self-organization (or spontaneous organization) as
well as for autopoetic processes within living organisms.
As an example of morphologicl computing phyllotaxis can be mentioned
that in botany is the arrangement of leaves on a plant stem (from ancient
Greek phýllon "leaf" and táxis "arrangement").
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“A specific crystalline order, involving the Fibonacci series, had until
now only been observed in plants (phyllotaxis). Here, these patterns are
obtained both in a physics laboratory experiment and in a numerical simulation. They arise from self-organization in an iterative process. They are
selected depending on only one parameter describing the successive appearance of new elements, and on initial conditions. The ordering is explained as due to the system’s trend to avoid rational (periodic) organization, thus leading to a convergence towards the golden mean.” Douady and
Couder (1992)
Morphological Computing in Robotics and Embodied AI
In recent years, morphological computing emerged as a new idea in robotics, (Pfeifer 2011), (Pfeifer and Iida 2005), (Pfeifer and Gomez 2009)
(Paul 2004). This presents a fundamental change compared with traditional
robotics which, based on the Cartesian tradition, treated separately the
body/machine and its control (computer) as completely independent elements of a robot. However, successively it has become evident that embodiment itself is essential for cognition, intelligence and generation of behaviour. In a most profound sense, embodiment is vital because cognition (and
consequently intelligent behaviour) results from the interaction of the
brain, body, and environment. (Pfeifer 2011) Instead of specifically controlling each movement of a robot, one can instead use morphological features of a body to automatically perform movements. Here we can take the
advantage of learning from biological forms and materials developed in
nature.
Specific forms of physical bodies of existing life forms have developed
evolutionary through optimization of their function in the environment. In
the development of an organism, based on the DNA code, body of a living
being is created through morphogenesis, which is governing a short time
scale formation of life. (Morphogenesis (from the Greek - “generation of
the shape"), is the biological process that causes an organism to develop its
shape.)
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On a long-time scale, morphological computing governs evolution of species. From an evolutionary perspective it is central that the environment
provides a physical source of biological body of an organism as well as a
source of energy and matter for its metabolism. Nervous system and the
brain of an organism evolve gradually through interactions (computational
processes) of a living agent with the environment. This process of mutual
shaping is a result of information self-structuring (Dodig Crnkovic 2008).
Here both physical environment and the physical body of an agent can in
every time instant be described by their informational structures.
The environment provides a variety of inputs in the form of both information and matter-energy, where the difference between information and
matter-energy is not in kind, but in type of use organism makes out of it.
As there is no information without representation, all information is carried
by some physical carrier (light, sound, radio-waves, chemical molecules
able to trigger smell receptors etc.). The same object can be used by an organism as a source of information and a source of nourishment/matter/energy. Some signals (say light) are used by some organisms
just as information necessary for orientation in the environment, while the
others use the light for photosynthetic processes in their metabolism. Thus
the question what will be used “only” as information and what as food/ energy source depends on the type of organism. In general, the simpler the
organism, the simpler the information structures of its body, the simpler information carriers it relies on, and simpler interactions with the environment.
The environment is not only a resource but at the same time it imposes
constraints which limit the space of possibilities for an agent. In an agent
that can be described as a complex informational structure, constraints imposed by the environment are driving time development (the computation)
of the structure, and thus even agent’s shape and behaviour, to specific trajectories.
This relationship between an agent and its environment is called structural
coupling by (Maturana & Varela 1980) and described by (Quick and Dautenhahn 1999) as “non-destructive perturbations between a system and its
environment, each having an effect on the dynamical trajectory of the oth-
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er, and this in turn effecting the generation of and responses to subsequent
perturbations.”
This mutual coupling between living systems and the environment can be
followed on the geological time scale, through the development of the first
life on Earth. It is believed that the first most primitive photosynthetic organisms contributed to the change of the environment and produced oxygen and other compounds necessary for life on earth. Catling et al. (2001)
explain how photosynthesis splits water into O2 and H, and methanogenesis transfers the H into CH4. Hydrogen escape after CH4 photolysis therefore causes a net gain of oxygen. This process may help explain how
Earth's surface environment became irreversibly oxidized which enabled
the development of life on Earth.
When it comes to human development, Clark (1997) p. 163 talks about
"the presence of continuous mutually modulatory influences linking brain,
body and world." When talking about living beings in general, it would be
the presence of continuous mutually shaping interactions between body
and environment, where body in some organisms developed nervous system and brain as control mechanisms.
In morphological computing modelling of the agents behaviour (such as
locomotion 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 environment. Through the embodied 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 principles of morphological computing (physical
computing) and data self-organization apply to biology and robotics.
Morphology is the central idea in understanding of the connection between
computation and information. Materials also represent morphology, just on
the more basic level of organization – the arrangements of molecular and
atomic structures. What appears as a form on a more fundamental level of
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organization (e.g. an arrangement of atoms), represents “matter” as a higher-order phenomenon (e.g. a molecule).
Isomers show how morphological forms are critical in pharmacology
where matching of a “drug” to a “receptor” is sought, which only is found
if forms are the right ones.
Info-computational naturalism (Dodig Crnkovic 2009) 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 itself is a process of morphological computation on
structures of organisms on a long-term scale. It will be instructive within
the info-computational framework to study in detail processes of self organization of information in an agent (as well as in population of agents)
able to re-structure themselves through interactions with the environment
as a result of morphological (morphogenetic) computation.
Morphological computing is information (re)structuring through computational processes which follow/implement physical laws. It is physical
computing or natural computing in which physical objects perform computation. Symbol manipulation in this case is physical object manipulation.
Different types of morphological computing interesting in AI and robotics:
1) Physical objects as “models of themselves”; physics directly
providing behavior of robotic body instead of detailed control of
movement (passive walker, grasping hand, etc.). Physical equivalent of symbolic computational control.
2) Subsymbolic computing performed by information-processing
adaptive learning system instead of advanced detailed planned
centrally-controlled pre-programmed behavior that has problems
in unpredictable environments. Understanding computational nature of continuous sub-symbolic information processing that enables living agents to survive in the environment.
3) Complementarity of discrete and continuous representations.
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Cognition as Morphological Computing by Restructuring of an
Agent’s Information Processing System in the Interaction with
the Environment
As a result of evolution, increasingly complex living organisms arise that
are able to survive and adapt to their environment. It means that they are
able to register inputs (data) from the environment, to structure those into
information, and in more complex organisms to structure information into
knowledge. The evolutionary advantage of using structured, componentbased approaches such as data – information – knowledge is improving response-time and efficiency of cognitive processes of an organism.
All cognition is embodied cognition in all living beings – microorganisms
as well as humans. In more complex cognitive agents, knowledge is built
upon not only direct reaction to input information, but also on intentional
information processes governed by choices, dependent on value systems
stored and organized in agent’s memory which is “implemented” in the
agent’s body.
Information and its processing are essential structural and dynamic elements which characterize structuring of input data (data  information 
knowledge) by an interactive computational process going on in the agent
during the adaptive interplay with the environment.
There is a continuum of morphological development from simplest living
organism’s automaton-like structures to most complex life form’s elaborate interplay between body, nervous system and brain and the environment. Cognition thus proceeds through restructuring of an agent in the interaction with the environment where this restructuring can be identified as
morphological computing.
Conclusions
The process of modeling, designing and creating robots that are more lifelike in their morphological properties, can both advance our understanding
of biological life and improve embodied and embedded cognition and intelligence in artificial agents. Morphological computing is a model of
computing, i.e. data/information processing, a type of natural (physical)
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computing and as a model it has both important practical and theoretical
implications.
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@inbook{Burgin_Dodig-Crnkovic_2011, place={New York/London/Singapore},
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author={Burgin, Mark and Dodig-Crnkovic, Gordana}, year={2011}, pages={vii
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Some additional threads:
We can see all of natural computing as based on computational agents exchanging messages (information). Elementary particles communicate via
force carriers. In physics (quantum field theory) the exchange of energy in
interactions is explained by force carriers – bosons: graviton and the pho-
16
ton for the long range forces of gravitation and electromagnetic force. The
W+, W- and Z bosons are the carriers for the weak force, and the gluon for
the strong force. Within an atom, nucleons in an atomic nucleus communicate via strong and weak force carriers. Nucleus (positively charged) and
electrons (negatively charged) of an atom are kept together by exchange of
electromagnetic force carriers. Externally atoms communicate via electromagnetic force carriers. Molecules consist of atoms and communicate via
messages of many different kinds – from atoms to electrical and chemical
force carriers. Cells of an organism communicate internally through metabolic networks, protein–protein interaction networks and with the DNA
code translated into proteins. At the same time cells are communicating
externally by exchanging molecules and other electrochemical signals and
so on. Humans are complex organisms consisting of numerous subsystems
with their own ways of internal communication all of it can be seen as natural computing of different sorts. Externally, humans communicate via different languages and various signals. Communication between humans is
mediated by very complex messages, unlike messaging in the physical
substrate which is significantly simpler and more predictable. However,
study of network behavior of humans reveals much regularity as well.
Black Hole Computers
In keeping with the spirit of the age, researchers can think of the
laws of physics as computer programs and the universe as a computer
By Seth Lloyd and Y. Jack Ng
What is the difference between a computer and a black hole? This
question sounds like the start of a Microsoft joke, but it is one of the
most profound problems in physics today. Most people think of
computers as specialized gizmos: streamlined boxes sitting on a desk
or fingernail-size chips embedded in high-tech coffeepots. But to a
physicist, all physical systems are computers. Rocks, atom bombs
and galaxies may not run Linux, but they, too, register and process
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information. Every electron, photon and other elementary particle
stores bits of data, and every time two such particles interact, those
bits are transformed. Physical existence and information content are
inextricably linked. As physicist John Wheeler of Princeton University says, "It from bit."
Black holes might seem like the exception to the rule that everything
computes. Inputting information into them presents no difficulty, but
according to Einstein's general theory of relativity, getting information out is impossible. Matter that enters a hole is assimilated, the
details of its composition lost irretrievably. In the 1970s Stephen
Hawking of the University of Cambridge showed that when quantum mechanics is taken into account, black holes do have an output:
they glow like a hot coal. In Hawking's analysis, this radiation is
random, however. It carries no information about what went in. If an
elephant fell in, an elephant's worth of energy would come out--but
the energy would be a hodgepodge that could not be used, even in
principle, to re-create the animal.
Molecular evolution is in part a process of evolution at the scale of
DNA, RNA, and proteins. Molecular evolution emerged as a scientific field in the 1960s as researchers from molecular biology, evolutionary biology and population genetics sought to understand recent
discoveries on the structure and function of nucleic acids and protein. Some of the key topics that spurred development of the field
have been the evolution of enzyme function, the use of nucleic acid
divergence as a "molecular clock" to study species divergence, and
the origin of noncoding DNA. (Wiki)
“I claim that Weinberg cannot deduce or simulate the coming into existence of the
heart in the universe. Nor is it obvious in what sense, if any, is the coming into existence in the universe of the heart “entailed” by the laws of physics.”
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Stuart Kauffman in the Introduction to the book "A Third Window: Natural Life
beyond Newton and Darwin" page
A Third Window: Natural Life beyond Newton and Darwin
by Robert E. Ulanowicz, Stuart A. Kauffman
http://www.scoop.co.nz/stories/HL1211/S00005/stu-kauffman-europes-origin-oflife-summit.htm