Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Complexity and Systems Science pp 1–25Cite as

Agent-Based Modeling and Artificial Life

  • Living reference work entry
  • First Online:

  • 562 Accesses

Definition of the Subject

Agent-based modeling began as the computational arm of artificial life some 20 years ago. Artificial life is concerned with the emergence of order in nature. How do systems self-organize themselves and spontaneously achieve a higher-ordered state? Agent-based modeling, then, is concerned with exploring and understanding the processes that lead to the emergence of order through computational means. The essential features of artificial life models are translated into computational algorithms through agent-based modeling. With its historical roots in artificial life, agent-based modeling has become a distinctive form of modeling and simulation. Agent-based modeling is a bottom-up approach to modeling complex systems by explicitly representing the behaviors of large numbers of agents and the processes by which they interact. These essential features are all that is needed to produce at least rudimentary forms of emergent behavior at the systems level. To...

This is a preview of subscription content, log in via an institution.

Abbreviations

Adaptation:

The process by which organisms (agents) change their behavior or by which populations of agents change their collective behaviors with respect to their environment.

Agent-based modeling (ABM):

A modeling and simulation approach applied to a complex system or complex adaptive system, in which the model is comprised of a large number of interacting elements (agents).

Ant colony optimization (ACO):

A heuristic optimization technique motivated by collective decision processes followed by ants in foraging for food.

Artificial chemistry:

Chemistry based on the information content and transformation possibilities of molecules.

Artificial life (ALife):

A field that investigates life’s essential qualities primarily from an information content perspective.

Artificial neural network (ANN):

A heuristic optimization and simulated learning technique motivated by the neuronal structure of the brain.

Autocatalytic set:

A closed set of chemical reactions that is self-sustaining.

Autonomous:

The characteristic of being capable of making independent decisions over a range of situations.

Avida:

An advanced artificial life computer program developed by Adami (Adami 1998) and others that models populations of artificial organisms and the essential features of life such as interaction and replication.

Biologically inspired computational algorithm:

Any kind of algorithm that is based on biological metaphors or analogies.

Cellular automaton (CA):

A mathematical construct and technique that models a system in discrete time and discrete space in which the state of a cell depends on transition rules and the states of neighboring cells.

Coevolution:

A process by which many entities adapt and evolve their behaviors as a result of mutually effective interactions.

Complex system:

A system comprised of a large number of strongly interacting components (agents).

Complex adaptive system (CAS):

A system comprised of a large number of strongly interacting components (agents) that adapt at the individual (agent) level or collectively at the population level.

Decentralized control:

A feature of a system in which the control mechanisms are distributed over multiple parts of the system.

Digital organism:

An entity that is represented by its essential information-theoretic elements (genomes) and implemented as a computational algorithm or model.

Downward causation:

The process by which a higher-order emergent structure takes on its own emergent behaviors and these behaviors exert influence on the constituent agents of the emergent structure.

Dynamic network analysis:

Network modeling and analysis in which the structure of the network, i.e., nodes (agents) and links (agent interactions), is endogenous to the model.

Echo:

An artificial life computer program developed by Holland (Holland 1975) that models populations of complex adaptive systems and the essential features of adaptation in nature.

Emergence:

The process by which order is produced in nature.

Entropy:

A measure of order, related to the information needed to specify a system and its state.

Evolution (artificial):

The process by which a set of instructions is transmitted and changes over successive generations.

Evolutionary game:

A repeated game in which agents adapt their strategies in recognition of the fact that they will face their opponents in the future.

Evolution strategies:

A heuristic optimization technique motivated by the genetic operation of selection and mutation.

Evolutionary algorithm:

Any algorithm motivated by the genetic operations including selection, mutation, crossover, etc.

Evolutionary computing:

A field of computing based on the use of evolutionary algorithms.

Finite state machine:

A mathematical model consisting of entities with a finite (usually small) number of possible discrete states.

Game of Life, Life:

A cellular automaton developed by Conway (Berlekamp et al. 2003) that illustrates a maximally complex system based on simple rules.

Generative social science:

Social science investigation with the goal of understanding how social processes emerge out of social interaction.

Genetic algorithm (GA):

A specific kind of evolutionary algorithm motivated by the genetic operations of selection, mutation, and crossover.

Genetic programming (GP):

A specific kind of evolutionary algorithm that manipulates symbols according to prescribed rules, motivated by the genetic operations of selection, mutation, and crossover.

Genotype:

A set of instructions for a developmental process that creates a complex structure, as in a genotype for transmitting genetic information and seeding a developmental process leading to a phenotype.

Hypercycle:

A closed set of functional relations that is self-sustaining, as in an autocatalytic chemical reaction network.

Individual-based model:

An approach originating in ecology to model populations of agents that emphasizes the need to represent diversity among individuals.

Langton’s ant:

An example of a very simple computational program that computes patterns of arbitrary complexity after an initial series of simple structures.

Langton’s loop:

An example of a very simple computational program that computes replicas of its structures according to simple rules applied locally as in cellular automata.

Learning classifier system (LCS):

A specific algorithmic framework for implementing an adaptive system by varying the weights applied to behavioral rules specified for individual agents.

Lindenmeyer system (L-system):

A formal grammar, which is a set of rules for rewriting strings of symbols.

Machine learning:

A field of inquiry consisting of algorithms for recognizing patterns in data (e.g., data mining) through various computerized learning techniques.

Mind-body problem:

A field of inquiry that addresses how human consciousness arises out of material processes and whether consciousness is the result of a logical-deductive or algorithmic process.

Meme:

A term coined by Dawkins (Dawkins 1989) to refer to the minimal encoding of cultural information, similar to the genome’s role in transmitting genetic information.

Particle swarm optimization:

An optimization technique similar to ant colony optimization, based on independent particles (agents) that search a landscape to optimize a single objective or goal.

Phenotype:

The result of an instance of a genotype interacting with its environment through a developmental process.

Reaction-diffusion system:

A system that includes mechanisms for both attraction and transformation (e.g., of agents) as well as repulsion and diffusion.

Recursively generated object:

An object that is generated by the repeated application of simple rules.

Self-organization:

A process by which structure and organization arise from within the endogenous instructions and processes inherent in an entity.

Self-replication:

The process by which an agent (e.g., organism, machine, etc.) creates a copy of itself that contains instructions for both the agent’s operation and its replication.

Social agent-based modeling:

Agent-based modeling applied to social systems, generally applied to people and human populations, but also animals.

Social network analysis (SNA):

A collection of techniques and approaches for analyzing networks of social relationships.

Stigmergy:

The practice of agents using the environment as a tool for communication with other agents to supplement direct agent-to-agent communication.

Swarm:

An early agent-based modeling toolkit designed to model artificial life applications.

Swarm intelligence:

Collective intelligence based on the actions of a set of interacting agents behaving according to a set of prescribed simple rules.

Symbolic processing:

A computational technique that consists of processing symbols rather than strictly numerical data.

Sugarscape:

An abstract agent-based model of artificial societies developed by Epstein and Axtell (Epstein and Axtell 1996) to investigate the emergence of social processes.

Tierra:

An early artificial life computer program developed by Ray (Ray 1991) that models populations of artificial organisms and the essential features of life such as interaction and replication.

Universal Turing machine (UTM):

An abstract representation of the capabilities of any computable system.

Update rule:

A rule or transformation directive for changing or updating the state of an entity or agent, as, for example, updating the state of an agent in an agent-based model or updating the state of an L-system.

Bibliography

Primary Literature

  • Adami C (1998) Introduction to artificial life. TELOS, Santa Clara

    Book  MATH  Google Scholar 

  • Alber MS, Kiskowski MA, Glazier JA, Jiang Y (2003) On cellular automaton approaches to modeling biological cells. In: Rosenthal J, Gilliam DS (eds) Mathematical systems theory in biology, communication, and finance, IMA volume. Springer, New York, pp 1–39

    Chapter  Google Scholar 

  • Alpaydın E (2004) Introduction to machine learning. MIT Press, Cambridge

    Google Scholar 

  • Axelrod R (1984) The evolution of cooperation. Basic Books, New York

    Google Scholar 

  • Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, Princeton

    Google Scholar 

  • Azzedine B, Renato BM, Kathia RLJ, Joao Bosco MS, Mirela SMAN (2007) An agent based and biological inspired real-time intrusion detection and security model for computer network operations. Comput Commun 30(13):2649–2660

    Article  Google Scholar 

  • Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, New York

    Google Scholar 

  • Berlekamp ER, Conway JH, Guy RK (2003) Winning ways for your mathematical plays, 2nd edn. AK Peters, Natick

    Google Scholar 

  • Bernaschi M, Castiglione F (2001) Design and implementation of an immune system simulator. Comput Biol Med 31(5):303–331

    Article  Google Scholar 

  • Bishop CM (2007) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  • Bobashev GV, Goedecke DM, Yu F, Epstein JM (2007) A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches. In: Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR (eds) Proceeding 2007 winter simulation conference, Washington, pp 1532–1537

    Google Scholar 

  • Bonabeau E (1997) From classical models of morphogenesis to agent-based models of pattern formation. Artif Life 3:191–211

    Article  Google Scholar 

  • Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York

    MATH  Google Scholar 

  • Carley KM, Fridsma DB, Casman E, Yahja A, Altman N, Chen LC, Kaminsky B, Nave D (2006) Biowar: scalable agent-based model of bioattacks. IEEE Trans Syst Man Cybern Part A: Syst Hum 36(2):252–265

    Article  Google Scholar 

  • Celada F, Seiden PE (1992) A computer model of cellular interactions in the immune system. Immunol Today 13(2):56–62

    Article  Google Scholar 

  • Clerc M (2006) Particle swarm optimization. ISTE Publishing, London

    Book  MATH  Google Scholar 

  • Dawkins R (1989) The selfish gene, 2nd edn. Oxford University Press, Oxford

    Google Scholar 

  • DeAngelis DL, Gross LJ (eds) (1992) Individual-based models and approaches in ecology: populations, communities and ecosystems. Proceedings of a symposium/workshop, Knoxville, 16–19 May 1990. Chapman & Hall, New York. ISBN 0-412-03171-X

    Google Scholar 

  • Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  • Dreyfus HL (1979) What computers can’t do: the limits of artificial intelligence. Harper & Row, New York

    Google Scholar 

  • Eiben AE, Smith JE (2007) Introduction to evolutionary computing, 2nd edn. Springer, New York

    Google Scholar 

  • Eigen M, Schuster P (1979) The hypercycle: a principle of natural self-organization. Springer, Berlin

    Book  Google Scholar 

  • Emonet T, Macal CM, North MJ, Wickersham CE, Cluzel P (2005) AgentCell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11):2714–2721

    Article  Google Scholar 

  • Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, Hoboken

    Google Scholar 

  • Epstein JM (2007) Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton

    Google Scholar 

  • Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. MIT Press, Cambridge

    Google Scholar 

  • Ermentrout GB, Edelstein-Keshet L (1993) Cellular automata approaches to biological modeling. J Theor Biol 160(1):97–133

    Article  Google Scholar 

  • Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, Hoboken

    MATH  Google Scholar 

  • Folcik VA, An GC, Orosz CG (2007) The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 4(39):1–18. http://www.tbiomed.com/content/4/1/39

  • Fontana W (1992) Algorithmic chemistry. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II: proceedings of the workshop on artificial life, Santa Fe, Feb 1990, Santa Fe Institute studies in the sciences of the complexity, vol X. Addison-Wesley, Reading, pp 159–209

    Google Scholar 

  • Gardner M (1970) The fantastic combinations of John Conway’s new solitaire game life. Sci Am 223:120–123

    Article  Google Scholar 

  • Gilbert N (2002) Varieties of emergence. In: Macal C, Sallach D (eds) Proceedings of the agent 2002 conference on social agents: ecology, exchange and evolution, Chicago, 11–12 Oct 2002, pp 1–11. Available on CD and at www.agent2007.anl.gov

  • Gilbert N, Troitzsch KG (1999) Simulation for the social scientist. Open University Press, Buckingham

    Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Goldberg DE (1994) Genetic and evolutionary algorithms come of age. Commun ACM 37(3):113–119

    Article  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan, Ann Arbor

    Google Scholar 

  • Holland J (1995) Hidden order: how adaptation builds complexity. Addison-Wesley, Reading

    Google Scholar 

  • Holland JH, Booker LB, Colombetti M, Dorigo M, Goldberg DE, Forrest S, Riolo RL, Smith RE, Lanzi PL, Stolzmann W, Wilson SW (2000) What is a learning classifier system? In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications. Springer, London, pp 3–32

    Chapter  Google Scholar 

  • Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, Oxford

    Google Scholar 

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, 840 pp

    MATH  Google Scholar 

  • Langton CG (1984) Self-reproduction in cellular automata. Physica D 10:135–144

    Article  ADS  Google Scholar 

  • Langton CG (1986) Studying artificial life with cellular automata. Physica D 22:120–149

    Article  MathSciNet  ADS  Google Scholar 

  • Langton CG (1989a) Preface. In: Langton CG (ed) Artificial life: proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, Los Alamos, Sept 1987, Addison-Wesley, Reading, pp xv–xxvi

    Google Scholar 

  • Langton CG (1989b) Artificial life. In: Langton CG (ed) Artificial life: the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, Los Alamos, Sept 1987, Santa Fe Institute studies in the sciences of complexity, vol VI. Addison-Wesley, Reading, pp 1–47

    Google Scholar 

  • Langton CG (1992) Life at the edge of chaos. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II: proceedings of the workshop on artificial life, Santa Fe, Feb 1990, Santa Fe Institute studies in the sciences of the complexity, vol X. Addison-Wesley, Reading, pp 41–91

    Google Scholar 

  • Le Novere N, Shimizu TS (2001) Stochsim: modelling of stochastic biomolecular processes. Bioinformatics 17(6):575–576

    Article  Google Scholar 

  • Lindenmeyer A (1968) Mathematical models for cellular interaction in development. J Theor Biol 18:280–315

    Article  Google Scholar 

  • Lucas JR (1961) Minds, machines and godel. Philosophy 36(137):112–127

    Article  Google Scholar 

  • Manson SM (2006) Bounded rationality in agent-based models: experiments with evolutionary programs. Int J Geogr Inf Sci 20(9):991–1012

    Article  Google Scholar 

  • Mehrotra K, Mohan CK, Ranka S (1996) Elements of artificial neural networks. MIT Press, Cambridge

    Google Scholar 

  • Mitchell M, Forrest S (1994) Genetic algorithms and artificial life. Artif Life 1(3):267–289

    Article  Google Scholar 

  • Ofria C, Wilke CO (2004) Avida: a software platform for research in computational evolutionary biology. Artif Life 10(2):191–229

    Article  Google Scholar 

  • Olariu S, Zomaya AY (eds) (2006) Handbook of bioinspired algorithms and applications. Chapman, Boca Raton, p 679

    MATH  Google Scholar 

  • Padgett JF, Lee D, Collier N (2003) Economic production as chemistry. Ind Corp Chang 12(4):843–877

    Article  Google Scholar 

  • Peacor SD, Riolo RL, Pascual M (2006) Plasticity and species coexistence: modeling food webs as complex adaptive systems. In: Pascual M, Dunne JA (eds) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New York, pp 245–270

    Google Scholar 

  • Penrose R (1989) The emperor’s new mind: concerning computers, minds, and the laws of physics. Oxford University Press, Oxford

    Google Scholar 

  • Poundstone W (1985) The recursive universe. Contemporary Books, Chicago, 252 pp

    Google Scholar 

  • Preziosi L (ed) (2003) Cancer modelling and simulation. Chapman, Boca Raton

    MATH  Google Scholar 

  • Ray TS (1991) An approach to the synthesis of life (tierra simulator). In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life Ii: proceedings of the workshop on artificial life. Wesley, Redwood City, pp 371–408

    Google Scholar 

  • Rechenberg I (1973) Evolutionsstrategie: optimierung Technischer Systeme Nach Prinzipien Der Biologischen evolution. Frommann-Holzboog, Stuttgart

    Google Scholar 

  • Sakoda JM (1971) The checkerboard model of social interaction. J Math Soc 1:119–132

    Article  Google Scholar 

  • Schelling TC (1971) Dynamic models of segregation. J Math Soc 1:143–186

    Article  Google Scholar 

  • Searle JR (1990) Is the brain a digital computer? Presidential Address to the American Philosophical Association

    Google Scholar 

  • Taub AH (ed) (1961) John Von Neumann: collected works. vol V: Design of computers, theory of automata and numerical analysis (Delivered at the Hixon Symposium, Pasadena, Sept 1948). Pergamon Press, Oxford

    Google Scholar 

  • Turing AM (1938) On computable numbers with an application to the entscheidungsproblem. Process Lond Math Soc 2(42):230–265

    MathSciNet  Google Scholar 

  • Turing AM (1952) The chemical basis of morphogenesis. Philos Trans Royal Soc B 237:37–72

    Article  ADS  Google Scholar 

  • von Neumann J (1966) In: Burks AW (ed) Theory of self-reproducing automata. University of Illinois Press, Champaign

    Google Scholar 

  • Wilke CO, Adami C (2002) The biology of digital organisms. Trends Ecol Evol 17(11):528–532

    Article  Google Scholar 

  • Wilke CO, Chow SS (2006) Exploring the evolution of ecosystems with digital organisms. In: Pascual M, Dunne JA (eds) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New York, pp 271–286

    Google Scholar 

  • Wolfram S (1984) Universality and complexity in cellular automata. Physica D 1–35

    Google Scholar 

  • Wolfram S (1999) The mathematica book, 4th edn. Wolfram Media/Cambridge University Press, Champaign

    MATH  Google Scholar 

  • Wolfram S (2002) A new kind of science. Wolfram Media, Champaign

    MATH  Google Scholar 

Books and Reviews

  • Artificial Life (journal) web page (2008) http://www.mitpressjournals.org/loi/artl. Accessed 8 Mar 2008

  • Banks ER (1971) Information processing and transmission in cellular automata. PhD dissertation, Massachusetts Institute of Technology

    Google Scholar 

  • Batty M (2007) Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. MIT Press, Cambridge

    Google Scholar 

  • Bedau MA (2002) The scientific and philosophical scope of artificial life. Leonardo 35:395–400

    Article  Google Scholar 

  • Bedau MA (2003) Artificial life: organization, adaptation and complexity from the bottom up. TRENDS Cognit Sci 7(11):505–512

    Article  Google Scholar 

  • Copeland BJ (2004) The essential turing. Oxford University Press, Oxford, 613 pp

    MATH  Google Scholar 

  • Ganguly N, Sikdar BK, Deutsch A, Canright G, Chaudhuri PP (2008) A survey on cellular automata. www.cs.unibo.it/bison/publications/CAsurvey.pdf

  • Griffeath D, Moore C (eds) (2003) New constructions in cellular automata, Santa Fe Institute studies in the sciences of complexity proceedings. Oxford University Press, New York, 360 pp

    MATH  Google Scholar 

  • Gutowitz H (ed) (1991) Cellular automata: theory and experiment. Special issue of Physica D. 499 pp

    Google Scholar 

  • Hraber T, Jones PT, Forrest S (1997) The ecology of echo. Artif Life 3:165–190

    Article  Google Scholar 

  • International Society for Artificial Life web page (2008) www.alife.org. Accessed 8 Mar 2008

  • Jacob C (2001) Illustrating evolutionary computation with mathematica. Academic, San Diego, 578 pp

    Google Scholar 

  • Michael CF, Fred WG, Jay A (2005) Simulation optimization: a review, new developments, and applications. In: Proceedings of the 37th conference on Winter simulation, Orlando

    Google Scholar 

  • Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, Princeton

    Google Scholar 

  • North MJ, Macal CM (2007) Managing business complexity: discovering strategic solutions with agent-based modeling and simulation. Oxford University Press, New York

    Book  Google Scholar 

  • Pascual M, Dunne JA (eds) (2006) Ecological networks: linking structure to dynamics in food webs, Santa Fe Institute studies on the sciences of complexity. Oxford University Press, New York

    Google Scholar 

  • Simon H (2001) The sciences of the artificial. MIT Press, Cambridge

    Google Scholar 

  • Sims K (1991) Artificial evolution for computer graphics. ACM SIGGRAPH ′91, Las Vegas, July 1991, pp 319–328

    Google Scholar 

  • Sims K (1994) Evolving 3D morphology and behavior by competition. Artif Life IV:28–39

    Google Scholar 

  • Terzopoulos D (1999) Artificial life for computer graphics. Commun ACM 42(8):33–42

    Article  Google Scholar 

  • Toffoli T, Margolus N (1987) Cellular automata machines: a new environment for modeling. MIT Press, Cambridge, 200 pp

    Google Scholar 

  • Tu X, Terzopoulos D (1994) Artificial fishes: physics, locomotion, perception, behavior. In: Proceedings of SIGGRAPH`94, 24–29 July 1994, Orlando, pp 43–50

    Google Scholar 

  • Weisbuch G (1991) Complex systems dynamics: an introduction to automata networks, translated from French by Ryckebusch S. Addison-Wesley, Redwood City

    Google Scholar 

  • Wiener N (1948) Cybernetics, or control and communication in the animal and the machine. Wiley, New York

    Google Scholar 

  • Wooldridge M (2000) Reasoning about rational agents. MIT Press, Cambridge

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Complex Adaptive Agent Systems Simulation (CAS2), Decision and Information Sciences Division, Argonne National Laboratory, Argonne, IL, USA

    Charles M. Macal

Authors
  1. Charles M. Macal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charles M. Macal .

Editor information

Editors and Affiliations

  1. Ramtech Ltd., Larkspur, California, USA

    Robert A. Meyers

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Macal, C.M. (2015). Agent-Based Modeling and Artificial Life. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_7-5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27737-5_7-5

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Online ISBN: 978-3-642-27737-5

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

Publish with us

Policies and ethics

Search

Navigation