Artificial immune system

In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.

Definition

The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system 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 and belonging to the broader field of Artificial Intelligence.

Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.[1]

AIS is distinct from computational immunology and theoretical biology 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 computation, such as DNA computing.

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.[2] 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) 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,[3][4] which is motivated by its hypothesized role in open ended learning and evolution.[5][6]

Originally AIS set out to find efficient abstractions of processes found in the immune system 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 [7] 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.

See also

Notes

  1. ↑ de Castro, Leandro N.; Timmis, Jonathan (2002). Artificial Immune Systems: A New Computational Intelligence Approach. Springer. pp. 57–58. ISBN 978-1-85233-594-6.
  2. ↑ Kephart, J. O. (1994). "A biologically inspired immune system for computers". Proceedings of Artificial Life IV: The Fourth International Workshop on the Synthesis and Simulation of Living Systems. MIT Press. pp. 130–139.
  3. ↑ Andrews and Timmis (2006). "A Computational Model of Degeneracy in a Lymph Node". Lecture Notes in Computer Science. 4163: 164. doi:10.1007/11823940_13.
  4. ↑ Mendao; et al. (2007). "The Immune System in Pieces: Computational Lessons from Degeneracy in the Immune System". Foundations of Computational Intelligence (FOCI): 394–400.
  5. ↑ Edelman and Gally (2001). "Degeneracy and complexity in biological systems". Proceedings of the National Academy of Sciences of the United States of America. 98 (24): 13763–13768. doi:10.1073/pnas.231499798.
  6. ↑ Whitacre (2010). "Degeneracy: a link between evolvability, robustness and complexity in biological systems". Theoretical Biology and Medical Modelling. 7 (6). doi:10.1186/1742-4682-7-6. Retrieved 2011-03-11.
  7. ↑ Dasgupta, Dipankar; Nino, Fernando (2008). CRC Press. p. 296. ISBN 978-1-4200-6545-9. Missing or empty |title= (help)
  8. ↑ de Castro, L. N.; Von Zuben, F. J. (2002). "Learning and Optimization Using the Clonal Selection Principle" (PDF). IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems. IEEE. 6 (3): 239–251. doi:10.1109/tevc.2002.1011539.
  9. ↑ Forrest, S.; Perelson, A.S.; Allen, L.; Cherukuri, R. (1994). "Self-nonself discrimination in a computer" (PDF). Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Los Alamitos, CA. pp. 202–212.
  10. ↑ Timmis, J.; Neal, M.; Hunt, J. (2000). "An artificial immune system for data analysis". BioSystems. 55 (1): 143–150. doi:10.1016/S0303-2647(99)00092-1. PMID 10745118.
  11. ↑ Greensmith, J.; Aickelin, U. (2009). "Artificial Dendritic Cells: Multi-faceted Perspectives" (PDF). Human-Centric Information Processing Through Granular Modelling: 375–395.

References

External links

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