artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech r ...

, artificial immune systems (AIS) are a class of computationally intelligent,

rule-based machine learning Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. The defining characteristic of a rule-based machine learne ...

systems inspired by the principles and processes of the vertebrate

immune system The immune system is a network of biological processes that protects an organism from diseases. It detects and responds to a wide variety of pathogens, from viruses to parasitic worms, as well as cancer cells and objects such as wood splint ...

. The algorithms are typically modeled after the immune system's characteristics of learning and

memory Memory is the faculty of the mind by which data or information is encoded, stored, and retrieved when needed. It is the retention of information over time for the purpose of influencing future action. If past events could not be remembered ...

for use in

problem-solving Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business an ...

Definition

The field of artificial immune systems (AIS) is concerned with abstracting the structure and function of the

to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of biologically inspired computing, and natural computation, with interests in

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

and belonging to the broader field of

. AIS is distinct from

computational immunology In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these probl ...

and

theoretical biology Mathematical and theoretical biology, or biomathematics, is a branch of biology which employs theoretical analysis, mathematical models and abstractions of the living organisms to investigate the principles that govern the structure, development a ...

that are concerned with simulating immunology using computational and mathematical models towards better understanding the immune system, although such models initiated the field of AIS and continue to provide a fertile ground for inspiration. Finally, the field of AIS is not concerned with the investigation of the immune system as a substrate for computation, unlike other fields such as

DNA computing DNA computing is an emerging branch of unconventional computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional electronic computing. Research and development in this area concerns theory, experiments, a ...

History

AIS emerged in the mid-1980s with articles authored by Farmer, Packard and Perelson (1986) and Bersini and Varela (1990) on immune networks. However, it was only in the mid-1990s that AIS became a field in its own right. Forrest ''et al.'' (on negative selection) and Kephart ''et al.'' published their first papers on AIS in 1994, and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on

clonal selection In immunology, clonal selection theory explains the functions of cells of the immune system (lymphocytes) in response to specific antigens invading the body. The concept was introduced by Australian doctor Frank Macfarlane Burnet in 1957, in an ...

) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999. Currently, new ideas along AIS lines, such as danger theory and algorithms inspired by the innate immune system, are also being explored. Although some believe that these new ideas do not yet offer any truly 'new' abstract, over and above existing AIS algorithms. This, however, is hotly debated, and the debate provides one of the main driving forces for AIS development at the moment. Other recent developments involve the exploration of degeneracy in AIS models, which is motivated by its hypothesized role in open ended learning and evolution. Originally AIS set out to find efficient abstractions of processes found in the

but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics problems. In 2008, Dasgupta and Nino published a textbook on immunological computation which presents a compendium of up-to-date work related to immunity-based techniques and describes a wide variety of applications.

Techniques

The common techniques are inspired by specific immunological theories that explain the function and behavior of the mammalian

adaptive immune system The adaptive immune system, also known as the acquired immune system, is a subsystem of the immune system that is composed of specialized, systemic cells and processes that eliminate pathogens or prevent their growth. The acquired immune system ...

. *

Clonal selection algorithm In artificial immune systems, clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity ...

: A class of algorithms inspired by the

theory of acquired immunity that explains how B and T

lymphocyte A lymphocyte is a type of white blood cell (leukocyte) in the immune system of most vertebrates. Lymphocytes include natural killer cells (which function in cell-mediated, cytotoxic innate immunity), T cells (for cell-mediated, cytotoxic ad ...

s improve their response to antigens over time called

affinity maturation In immunology, affinity maturation is the process by which TFH cell-activated B cells produce antibodies with increased affinity for antigen during the course of an immune response. With repeated exposures to the same antigen, a host will produc ...

. These algorithms focus on the

Darwinian Darwinism is a theory of biological evolution developed by the English naturalist Charles Darwin (1809–1882) and others, stating that all species of organisms arise and develop through the natural selection of small, inherited variations that ...

attributes of the theory where selection is inspired by the affinity of antigen–antibody interactions, reproduction is inspired by

cell division Cell division is the process by which a parent cell divides into two daughter cells. Cell division usually occurs as part of a larger cell cycle in which the cell grows and replicates its chromosome(s) before dividing. In eukaryotes, there ar ...

, and variation is inspired by

somatic hypermutation Somatic hypermutation (or SHM) is a cellular mechanism by which the immune system adapts to the new foreign elements that confront it (e.g. microbes), as seen during class switching. A major component of the process of affinity maturation, SHM div ...

. Clonal selection algorithms are most commonly applied to

optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...

and

pattern recognition Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics ...

domains, some of which resemble parallel

hill climbing numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solutio ...

and the genetic algorithm without the recombination operator. * Negative selection algorithm: Inspired by the positive and negative selection processes that occur during the maturation of

T cells A T cell is a type of lymphocyte. T cells are one of the important white blood cells of the immune system and play a central role in the adaptive immune response. T cells can be distinguished from other lymphocytes by the presence of a T-cell re ...

in the

thymus The thymus is a specialized primary lymphoid organ of the immune system. Within the thymus, thymus cell lymphocytes or ''T cells'' mature. T cells are critical to the adaptive immune system, where the body adapts to specific foreign invaders. ...

called T cell tolerance. Negative selection refers to the identification and deletion ( apoptosis) of self-reacting cells, that is T cells that may select for and attack self tissues. This class of algorithms are typically used for classification and pattern recognition problem domains where the problem space is modeled in the complement of available knowledge. For example, in the case of an

anomaly detection In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority o ...

domain the algorithm prepares a set of exemplar pattern detectors trained on normal (non-anomalous) patterns that model and detect unseen or anomalous patterns. * Immune network algorithms: Algorithms inspired by the idiotypic network theory proposed by

Niels Kaj Jerne Niels Kaj Jerne, FRS (23 December 1911 – 7 October 1994) was a Danish immunologist. He shared the Nobel Prize in Physiology or Medicine in 1984 with Georges J. F. Köhler and César Milstein "for theories concerning the specificity in dev ...

that describes the regulation of the immune system by anti-idiotypic antibodies (antibodies that select for other antibodies). This class of algorithms focus on the network graph structures involved where antibodies (or antibody producing cells) represent the nodes and the training algorithm involves growing or pruning edges between the nodes based on affinity (similarity in the problems representation space). Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks. * Dendritic cell algorithms: The dendritic cell algorithm (DCA) is an example of an immune inspired algorithm developed using a multi-scale approach. This algorithm is based on an abstract model of

dendritic cells Dendritic cells (DCs) are antigen-presenting cells (also known as ''accessory cells'') of the mammalian immune system. Their main function is to process antigen material and present it on the cell surface to the T cells of the immune system. The ...

(DCs). The DCA is abstracted and implemented through a process of examining and modeling various aspects of DC function, from the molecular networks present within the cell to the behaviour exhibited by a population of cells as a whole. Within the DCA information is granulated at different layers, achieved through multi-scale processing.

Notes

References

*J.D. Farmer, N. Packard and A. Perelson, (1986)
The immune system, adaptation and machine learning
, Physica D, vol. 2, pp. 187–204 *H. Bersini, F.J. Varela
Hints for adaptive problem solving gleaned from immune networks
Parallel Problem Solving from Nature, First Workshop PPSW 1, Dortmund, FRG, October, 1990. *D. Dasgupta (Editor), Artificial Immune Systems and Their Applications, Springer-Verlag, Inc. Berlin, January 1999, * V. Cutello and G. Nicosia (2002)
An Immunological Approach to Combinatorial Optimization Problems
Lecture Notes in Computer Science, Springer vol. 2527, pp. 361–370. *L. N. de Castro and F. J. Von Zuben, (1999) "Artificial Immune Systems: Part I -Basic Theory and Applications", School of Computing and Electrical Engineering, State University of Campinas, Brazil, No. DCA-RT 01/99. *S. Garrett (2005) "How Do We Evaluate Artificial Immune Systems?" Evolutionary Computation, vol. 13, no. 2, pp. 145–178. http://mitpress.mit.edu/journals/pdf/EVCO_13_2_145_0.pdf * V. Cutello, G. Nicosia, M. Pavone, J. Timmis (2007) An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 101–117. https://web.archive.org/web/20120208130715/http://www.dmi.unict.it/nicosia/papers/journals/Nicosia-IEEE-TEVC07.pdf *Villalobos-Arias M., Coello C.A.C., Hernández-Lerma O. (2004) Convergence Analysis of a Multiobjective Artificial Immune System Algorithm. In: Nicosia G., Cutello V., Bentley P.J., Timmis J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. DOI https://doi.org/10.1007/978-3-540-30220-9_19

External links

AISWeb: The Online Home of Artificial Immune Systems
Information about AIS in general and links to a variety of resources including ICARIS conference series, code, teaching material and algorithm descriptions.
ARTIST: Network for Artificial Immune Systems
Provides information about the UK AIS network, ARTIST. It provides technical and financial support for AIS in the UK and beyond, and aims to promote AIS projects.
Computer Immune Systems
Group at the University of New Mexico led by Stephanie Forrest.
AIS: Artificial Immune Systems
Group at the University of Memphis led by Dipankar Dasgupta.
IBM Antivirus Research
Early work in AIS for computer security. {{DEFAULTSORT:Artificial Immune System

Definition

History

Techniques

See also

Notes

References

External links