Positive and Negative Feedback in Dual-Species Emitter Interaction

Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation

The complexity of artificial life is produced not only via their genotype, but also by the environment that they thrive in. Past artificial life research focuses on the evolutionary behavior of artificial organisms from an intrinsic perspective of how concepts such as crossover and mutation increase the fitness of the organism, others focus on the emergent macrostate of a simulation that arises out of the local interaction of entities. This research aims to introduce an additional ‘affector’ into artificial life simulations, namely, an enhanced environment where an added dimension of complex behaviour can be produced through the use of environment emitters.



Ch’ng E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, 20-24 November 2012, Kobe, Japan. [working copy]

Agent-Based Modelling for Ecology

Agent-Based Modelling for Ecology

Understanding the complexity of past vegetation in space and through time requires a level of detail which begins at the level of the individual plant with its internal processes, such as tolerance to environmental factors, competition from other plants, reproduction, and dissemination of progenies. Whilst this may appear to be entirely unattainable using established methodologies, it can be approached through Agent-Based Modelling (ABM) or Individual-Based Modelling (IBM), also called Individual-Based Ecology (IBE), which has been described by Huston et al. (1988) as an approach with the potential to unify ecological theory. ‘Modelling agents’ implies determining the properties and functions associated with an individual entity (such as a plant) and how that entity interacts with both other entities and its environment. The research uses a ‘bottom-up’ approach founded on the principles of Complexity.

Publications

  • Ch’ng E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, 20-24 November 2012, Kobe, Japan.
  • E. Ch’ng (2011) Spatially Realistic Positioning of Plants for Virtual Environments: Simple Biotic and Abiotic Interaction for Populating Terrains. IEEE Computer Graphics and Applications 31(4), p66-77, July-Aug. 2011.
  • E. Ch’ng (2009) Ground Cover and Vegetation in Level Editors: Learning from Ecological Modelling, 14th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games, Louisville, Kentucky, USA.
  • Ch’ng, E. (2009) An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research, in Nature-Inspired informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, R. Chiong, Editor. 2009, IGI Global: Hershey, PA.
  • Ch’ng, E. (2009). “An Efficient Segmentation Algorithm For Entity Interaction.” Biodiversity Informatics, 6(1), p. 5-17. [link to article]
  • Ch’ng, E. (2009). “A Behavioural Agent Model for Synthesizing Vegetation Distribution Patterns on 3D Terrains.” Taylor & Francis, Applied Artificial Intelligence 23(1): 78-102. [manuscript]
  • Ch’ng, E. (2007). Modelling the Adaptability of Biological Systems. The Open Cybernetics and Systemics Journal 1: 13 – 20.
  • Ch’ng E., and Stone R.J., (2006). 3D Archaeological Reconstruction and Visualization: An Artificial Life Model for Determining Vegetation Dispersal Patterns in Ancient Landscapes. IEEE Computer Society. Computer Graphics, Imaging and Vision, CGiV’06, 25-28 July 2006, Sydney, Australia.
  • Ch’ng E. and Stone R.J., (2006). Enhancing Virtual Reality with Artificial Life: Reconstructing a Flooded European Mesolithic Landscape. Presence: Teleoperators and Virtual Environments, June 2006 Special Issue on Virtual Heritage, Presence 15 (3), pp. 341-352.
  • Ch’ng E., Stone R.J., Arvanitis T.N., (2005). Evaluating Artificial Life-based Vegetation Dynamics in the Context of a Virtual Reality Representation of Ancient Landscapes. Virtual Systems and Multimedia, VSMM2005. Ghent, Belgium Oct 3-6, 2005, pp 112-118.

 

Artificial Life

Artificial Life

The phrase Artificial Life or alife is coined by Christopher Langton in the Proceedings of the Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems [1]. The field owes its deepest intellectual roots to John von Neumann and Norbert Wiener [2]. Von Neumann [3] was the first to design an Artificial Life model without referring to it as such. He attempted to understand the fundamental properties of living systems by creating his self-reproducing Cellular Automata (CA) that exhibited those properties. Wiener [4] at about the same time started applying information theory and the analysis of self-regulatory processes (homeostasis) to the study of living systems. According to Bedau [2], both the constructive and abstract methodology of cellular automata and the abstract and material-independent methodology of information theory still exemplify much of alife.

Artificial Life as defined by Langton [1], is “…the study of man-made systems that exhibit behaviours characteristic of natural living systems. It complements the traditional biological sciences concerned with the analysis of living organisms by attempting to synthesize life-like behaviours within computers and other artificial media. By extending the empirical foundation upon which biology is based beyond the carbon-chain life that has evolved on Earth, Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be.” It is simply “life made by humans rather than by nature”.  However, in later years, Langton states that “Artificial Life is many things to many people” [5]; he also suggests that “It is probably safe to say that the field as a whole represents attempt to increase vastly the role of synthesis in the study of biological phenomena.” Rietman [6], on ‘Creating Artificial Life’ gave a short list of biological life that might consist of reproduction, metabolism, evolution, growth, self-repair, and adaptability. A practical goal of alife is to find a mechanism for an evolutionary process to be used in the automatic design and creation of artefact [7].

The history of the formal studies in AI is significantly earlier than alife. As such some have attempted to classify alife research as being a subfield of AI. Even though studies in alife overlap with those in AI, both approach the subject rather differently. The studies in AI aim to model the human mind whereas alife attempts at modelling life. The former uses the top-down approach whereas the latter uses the bottom-up approach of emergent systems. In fact, research in Complex Adaptive Systems (CAS) and alife are parallel.

Artificial Life research is divided into two main groups. The Strong alife position suggests that “life is a process which can be abstracted away from any particular medium” (John von Neumann) and can be created by the use of inorganic matter via some simple initial conditions or simple mechanical rules. The Weak alife position suggests that life cannot be generated outside of a carbon-based chemical solution but its processes could be understood by mimicking it in computer simulations.

Bedau [8] identified the three branches of alife:

  • Soft alife creates simulations or other purely digital constructions that exhibit life-like behaviour
  • Hard alife produces hardware implementations of life-like systems
  • Wet alife synthesises living systems out of biochemical substances

The work in alife addresses two issues:

  • The study of life beyond the carbon-chain chemistry in biological life
  • The application of the principles of life for problem solving

Biology is the scientific study of carbon-based life forms. The fundamental obstacle in theoretical biology is that it is impossible to derive general principles from single examples. In order to derive general theories and to distinguish the essential properties of life, comparisons had to be made from many instances of life. Time is also an obstacle. The study of the life-cycles of organisms and their genetic descent requires the element of time. The other factor is data-noise. The compounds of data that can be gathered in the study of life inevitably results in information noises. This obstacle frustrates analysis of matter that requires unpolluted datasets. Artificial Life resolves these issues by creating alternative life-forms within computers. Computer simulation has permitted a new approach to the study of evolution and natural systems. According to Mitchell [9], simulation can be controlled, repeated to see how the modification of certain parameters changes the behaviour of the simulation, and run for many simulated generations. Such great control over synthesised life within computers has given researchers the means to eliminate the limitations of time. It has also allowed the omission of information noises by filtering parameters that are not required so that a better understanding of life can be realised as a result of an unpolluted computer generated environment.

According to Christopher Langton, Artificial Life is “the study of synthetic systems that exhibit behaviors characteristic of natural living systems. It complements the traditional biological sciences concerned with the analysis of living organisms by attempting to synthesise life-like behavior within computers and other artificial media. By extending the empirical foundation upon which biology is based beyond the carbon-chain life that evolved on Earth, Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be” [10]. Studies in alife are similar to biology – alife is an experimental science. It attempts to understand the mechanisms behind living systems by synthesising them so that the structure and function of these systems may be understood and applied. Langton again states that “By extending the horizons of empirical research in biology beyond the territory currently circumscribed by life-as-we-know-it, the study of Artificial Life gives us access to the domain of life-as-it-could-be, and it is within this vastly larger domain that we must ground general theories of biology and in which we will discover novel and practical applications of biology in our engineering endeavors.”

Artificial Life is concerned with generating life-like behaviours within computers. Since the study focuses on the behaviours of life, it involves identifying the intrinsic mechanisms that generate such behaviours. These mechanisms are often very simple rules within an organism out of which complex behaviour emerges. Or on a higher level, the mechanism itself may be the simple behaviours of an organism. Together as cooperative entities, these organisms worked together as a larger ‘organism’ for the survival of the colony, yet from very basic pre-programmed rules in the monotony or variety of individual behaviours. The phenomena or intelligence that emerges as a result of the simple interaction between individual entities is called emergence [11, 12], a central concept supporting studies in alife.

Systems that exhibit emergent behaviours are commonly expressed in the sentence “the whole is greater than the sum of its parts” [13]. The term emergence was first used by an English philosopher G.H. Lewes [14] over a hundred years ago and has been subsequently studied in philosophy [15-18]. However, the process that issued in emergent behaviour or property was unknown to them until the advent of computers and the initiation of the theory of complexity [9-11] coupled with experiments on Cellular Automata (CA) [13, 22-25]. Studies in CA showed that simple rules in computer agents could give rise to complex behaviours. A particular CA ‘Life rule’ [16] invented by John Conway in the 1970s called the Game of Life demonstrated the idea of emergence where simple interactions between entities resulted in objects and patterns that are surprising and counter-intuitive. Referring to the definition of the term ‘emergence’, John Holland [11], a pioneer in complex systems and nonlinear science advised that “It is unlikely that a topic as complicated as emergence will submit meekly to a concise definition, and I have no such definition to offer”, however, he adds that “The hallmark of emergence is this sense of much coming from little… where the behaviour of the whole is much more complex than the behaviour of the parts.” Another definition [27] states that emergence is “…the process by which patterns or global-level structures arise from interactive local-level processes. This “structure” or “pattern” cannot be understood or predicted from the behaviour or properties of the component units alone.” In a more elaborate sentence given by Stacey [18], “Emergence is the production of global patterns of behaviour by agents in a complex system interacting according to their own local rules of behaviour, without intending the global patterns of behaviour that come about. In emergence, global patterns cannot be predicted from the local rules of behaviour that produce them. To put it another way, global patterns cannot be reduced to individual behaviour.”

The success of alife in problem solving is identified by the possibility of emulating nature’s properties of decentralisation, self-organisation, self-assembly, self-producing, and self-reproduction, all of which are key concepts in the science of alife. A decentralised system [29] relies on lateral relationships for decision making instead of on the hierarchical structure of command or force. Self-organisation [13, 30] refers to systems that manage itself and increases in productivity automatically without guidance from an external source. In systems with characteristics of self-assembly, patterns are seen to form from simple disordered components. Self-producing or autopoiesis [31] connotes the idea that certain types of system continuously produce their constituents, their own components, which then participate in these same production processes. An autopoietic system has a circular organisation, which closes on itself, its outputs becoming its own inputs. Such systems possess a degree of autonomy from their environment since their own operations ensure, within limits, their future continuation. In Self-replication [23, 32, 33], entities makes copies of themselves.

Resnick [29] presents evidence of a trend of decentralisation across important domains in nature, organisation, society, science, and philosophy. He states that orderly patterns can arise from simple, local interactions and that many things are “organised without an organisation, coordinated without a coordinator”. In the same way, Farmer and Packard [34] noted that in self-organising systems, patterns emerge out of lower-level randomness. Furthermore, Charles Darwin [35] asserted that order and complexity arises from decentralised processes of variation and selection. Langton [36], the founder of alife, stated that in order to model complex systems like life, the most promising approach is to dispense “with the notion of a centralised global controller” and to focus “on mechanisms for the distributed control of behaviour”.

The characteristics inherent in many decentralised systems are nonlinear, and the nonlinearity, as characterised by natural and alife systems is what makes the outcome more than the sum of their parts – forming dynamic patterns, collective intelligence and unpredictable behaviours from the lateral interactions between similar entities. This is the difference between the bottom-up approach in alife and the top-down methods in AI and many older man-made systems. By employing the bottom-up approach observed in nature, not only can we discover how certain systems work, but many complex problems previously impenetrable with top-down methods can now be readily solved.

Eugene Ch’ng, June 2005

References:

[1]    C.G. Langton, ed. Artificial Life, Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. 1989, Addison-Wesley Publishing: Redwood City.

[2]    M.A. Bedau, Artificial Life: Organization, adaptation and complexity from the bottom up.. Trends in Cognitive Sciences, 2003. 7(11): p. 505-512.

[3]    J. von Neumann, Burks, A.W., Theory of Self-Reproducing Automata. (Urbana IL: University of Illinois Press, 1966).

[4]    N. Wiener, Cybernetics, or Control and Communication i nthe Animal and the Machine. Wiley, 1948).

[5]    C.G. Langton, ed. Artificial Life: An Overview. 1995, MIT Press: Cambridge.

[6]    E. Rietman, Creating Artificial Life: Self Organization. (Blue Ridge Summit, PA: Windcrest Books/McGraw Hill, 1993).

[7]    J. Vaario, Artificial life primer (Technical Report TR-H-033). (Kyoto: ATR Human Information Processing Research Labs, 1993).

[8]    M.A. Bedau, Artificial Life: Organization, adaptation and complexity from the bottom up.. Trends in Cognitive Sciences, 2003. 7(11): p. 505-512.

[9]    M. Mitchell, An Introduction to Genetic Algorithms (Complex Adaptive Systems). The MIT Press, 1998).

[10]    C.G. Langton, Artificial Life. (Boston, MA: Addison-Wesley Longman Publishing Co., Inc., 1990).

[11]    J. H. Holland, Emergence from Chaos to order. (Oxford: Oxford University Press, 1998).

[12]    S Johnson, Emergence: The Connected Lives of Ants, Brains, Cities, and Software. Sribner, 2002).

[13]    S. Kauffman, At Home in the Universe: The search for laws of complexity. (Harmondsworth: Penguin, 1996).

[14]    G.H. Lewes, Problems of Life and Mind. (London: Kegan Paul, Trench, Turbner, & Co., 1875).

[15]    B.P. McLaughlin, The Rise and Fall of British Emergentism. Emergence or Reduction?: Essays on the Prospects of Nonreductive Physicalism. (Berlin: Walter de Gruyter, 1992).

[16]    J.S. Mill, System of Logic. 8th ed (London: Longmans, Green, Reader, and Dyer, 1843).

[17]    E. Nagel, The Structure of Science. (New York: Harcourt, Brace and Wilson, 1961).

[18]    J. Goldstein, Emergence as a Construct: History and Issues. 1, 1999. 1: p. 49-72.

[19]    M. M. Waldrop, Complexity: The Emerging Science at the Edge of Order and Chaos. (London: Viking, 1993).

[20]    R. Lewin, Complexity: Life on the Edge of Chaos. (London: Phoenix, 1993).

[21]    J. H. Holland, Hidden Order: How adaptation builds complexity. Helix Books, Addison-Wesley Publishing, 1995).

[22]    J. von Neumann, Burks, A.W., Theory of Self-Reproducing Automata. (Urbana IL: University of Illinois Press, 1966).

[23]    C.G. Langton, “Studying Artificial Life with Cellular Automata, Evolution, Games and Learning: Models of Adaptation in Machines and Nature, Proceedings of the Fifth Annual Conference of the Centre for Nonlinear Studies. 1986. Los Alamos: Amsterdam: North-Holland,p. 120-149

[24]    T. Toffoli and N. Margolus, Cellular automata machines: A new environment for modelling. (Cambridge: MIT Press, 1987).

[25]    H. Gutowitz, Cellular automata: Theory and experiment. Physica D, 1991. 45: p. 1-3.

[26]    M. Resnick, Silverman, B., The Facts of Life. 1996, http://llk.media.mit.edu/projects/emergence/life-intro.html Last accessed 3 January 2006, 2006.

[27]    K. Mihata, The Persistence of ‘Emergence’, in Chaos, Complexity & Sociology: Myths, Models & Theories, A.E. Raymond, Horsfall, S., Lee., M.E., Editor. 1997, Thousand Oaks: California: Sage. p. 30-38.

[28]    R. Stacey, Complexity and Creativity in Organizations. (San Francisco: Berrett-Koehler, 1996).

[29]    M. Resnick, Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. (Cambridge, Massachusetts: MIT Press, 1994).

[30]    S.A. Kauffman, The Origins of Order: Self-Organization and Selection in Evolution. (Oxford: Oxford University Press, 1993).

[31]    J. Mingers, Self-Producing Systems: Implications and Applications of Autopoiesis. (New York and London: Plenum Press, 1995).

[32]    M. Sipper, Fifty years of research on self-replication: an overview. Artificial Life, 1998. 4: p. 237-257.

[33]    M. Sipper and J.A. Reggia, Go forth and replicate. Sci. Am., 2001. 285: p. 34-43.

[34]    D. Farmer, Packard, N., Evolution, Games, and Learning: Models for Adaptations in Machines and Nature. Physica D, 1986. 22D(1).

[35]    C Darwin, The oigin of species by means of natural selection. (LONDON: JOHN MURRAY, 1859).

[36]    C.G. Langton, ed. Artificial Life, Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems. 1989, Addison-Wesley Publishing: Redwood City.

Multitouch Table Developments

Multitouch Interactive 3D Objects and Environments

Multitouch adds another dimension to interactive 3D objects and environments. Additional touches on screens allows natural gestures and multiple users to interact with real-time 3D.

 

 

Publications

  • Ch’ng, E. (in press) The Mirror Between Two Worlds: 3D Surface Computing Interaction for Digital Objects and Environments, in Harrison, D. (ed.), Digital Media and Technologies for Virtual Artistic Spaces, IGI Global.
  • Ch’ng, E. (2012) New Ways of Accessing Information Spaces Using 3D Multitouch Tables. Proceedings of the Art, Design and Virtual Worlds Conference, Cyberworlds2012 , Darmstadt, Germany, 25-27 September 2012.

Exhibition

  • Mesolithic Survival Interactive Simulation, Drowned Landscapes, Royal Society Summer Exhibition 3-8 July 2012.

 

Heritage Digital Reconstruction

Digital Heritage Interactive 3D for Monuments, Landscapes and Sites

GIS and 3D laser-scanned based digital reconstructions of monuments, landscapes and sites for archaeological interpretation and exploration.

Publications

  • E.Ch’ng (in press) Digital Heritage Tourism: Reconfiguring the Visitor Experience in Heritage Sites, Museums and Architecture in the Era of Pervasive Computing, Percorsi creativi di turismo urbano (Creative Paths of Urban Tourism) Conference, Catania, 22-24 September 2011.
  • Gearey, B., Chapman, H., Krawiec, K., Howard, A. Ch’ng, E. (2012) Head of the river, source of the sea… Iron Age Timber Alignments in the Waveney Valley, East England. 18th Annual Meeting of the European Association of Archaeologists EAA Conference, 29 August – 1 September 2012.
  • E. Ch’ng, Chapman H., Gaffney C., Gaffney V., Murgatroyd P. and Neubauer W. (2011) Landscapes without Figures: Big data, Long waves and the Formative Role of Archaeological Computing. IEEE Computer: Special Issue on Computational Archeology.
  • E. Ch’ng (2009) “Experiential Archaeology: Is Virtual Time Travel Possible?” Journal of Cultural Heritage, vol. 20, pp. 458-470, 2009.
  • Ch’ng E. (2007) Using Games Engines for Archaeological Visualisation: Recreating Lost Worlds, CGames ’07, The 11th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games, La Rochelle, France. 21-23 November 2007.

Projects

  • Flag Fen Bronze Age Reconstruction for Vivacity
  • Geldeston, Norfolk Iron Age Reconstruction
  • Stonehenge Landscape Reconstruction
  • Wooden Henge Virtual Reconstruction
  • Shotton River, North Sea Reconstruction
Complexity Science

Complexity

Complexity science studies systems which has a population of interacting entities. Theories in complexity complement classical mechanistic perceptions with the aim of explaining the broader phenomenon of a system or systems. Following the reductionist approach of René Descartes that the complexity of the world can be understood by reducing them to the interaction of parts or simpler things, the notion given by Sir Isaac Newton that “Truth is ever to be found in simplicity, and not in the multiplicity and confusion of things” and the idea of a ‘clockwork universe’ has influenced past scientific thinking. It was understood that once you have analysed phenomenon in their simplest components “their evolution will turn out to be perfectly regular, reversible and predictable, while the knowledge you gained will merely be a reflection of that pre-existing order”. As scientific explorations expanded into the study of living organisms and populations in different order however, the mechanistic conception has reached a limit in its ability to yield new knowledge, as far as complex systems are concerned.

“Complex” describes a system as consisting of many different and connected parts; it is a network, a group or a system of different entities that are linked in a close or complicated way – according to Oxford Dictionaries. Complex adaptive systems are present in every levels of the hierarchy of life – at the molecular level to individual organisms and metapopulation, and from community to the global environment. The intricate relationships at the micro level and the emergent behaviour found at the meso and the macro levels cannot be understood by analysing the microstates alone, for “the whole is more than the sum of its parts” (Aristotle, Metaphysica 10f-1045a). Complex systems require both a holistic and a reductionist approach in the epistemology; the latter is needed at least in the modelling of it, in order to understand the phenomena that are manifested in various levels of organisation. Although the variation of complex systems is vast, they share common characteristics such as emergence and self-organisation (De Wolf, 2005) that resulted from the interaction of simple rules. Complex systems are found at ‘the edge of chaos’ (Lewin, 1993), they exist between order and disorder, they are predictable and at times unpredictable; miniscule changes in initial conditions produces massive stochastic revolutions at spatial and temporal scales. These features may give us a ‘handle’ from which we can grasp illusive behaviour at the systemic level for creating models and for understanding phenomenon. How local communication between agents gives rise to collective behaviour is an important study area. At present however, the inner workings of many complex systems are yet unknown. Otherwise, issues related to epidemics, medical treatment, society, the economy, ecology, and etc. would have been resolved. Agent-based modelling and simulation can provide a platform from which phenomenon can be understood within our lifetime, at a flexible spatial and temporal magnitude, at liberty from ethical and environmental constraints, and free from noise. We must not forget that a model is a model, however, new information can be acquired and conclusions can be made from hypothesis generation and testing. The foundation of understanding complex systems requires computer modelling, for we are dealing with non-linear interactions within a population of entities. These interactions are mainly information and energy communications. They are difficult to model and predict with state-variable techniques (difference and differential equations) as changes occur at different levels and at certain resolutions.

Modelling complex systems necessarily implies that we are interested in determining the properties, functions, and principles associated with an individual entity, and how that entity interacts and communicates information with other entities and its environment. We must not however, observe behaviours at a local level only. The collective behaviour at multiple levels (micro-meso-macro) will need to be analysed. This is what is meant by the need for both a holistic and a reductionist approach. Such a modelling procedure reflects how complex systems in nature work, it is bottom-up . This can be contrasted with ‘top-down approaches’ where models are governed by a central system which plans, arranges, and tunes patterns (See Ch’ng, 2009 for a survey of traditional statistical and predictive modelling methods).

 

 

See a collection of my Scoop.it curated pages on Complexity in general.

Macro-Micro Agent and Environment Modelling

Macro-Micro Agent-Environment Interaction

Macro to micro environment modelling and agent interactions in relation to the effective environmental factors (temperature, light, chemical factors, etc).

Publications

  • Ch’ng E. (accepted) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, 20-24 November 2012, Kobe, Japan.
  • Ch’ng, E. (2007). Modelling the Adaptability of Biological Systems. The Open Cybernetics and Systemics Journal 1: 13 – 20.