Biological Inspiration for Artificial Immune Systems (original) (raw)

Conceptual frameworks for artificial immune systems

2005

We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of Artificial Immune System (AIS) network models, and we discuss mathematical techniques for analysing the state dynamics of AIS. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally discuss a case study, and hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.

An Introduction to Artificial Immune Systems

Handbook of Natural Computing, 2012

The field of artificial immune systems (AIS) can be thought of comprising two threads of research: the employment of mathematical and computational techniques in the modelling of immunology, and the incorporation of immune system inspired mechanisms and metaphors in the development of engineering solutions. The former permits the integration of immunological data and sub-models into a coherent whole, which may be of value to immunologists in the facilitation of immunological understanding, hypothesis testing, and the direction of future research. The latter attempts harness the perceived properties of the immune system in the solving of engineering problems. This chapter concentrates on the latter: the development and application of immune inspiration to engineering solutions.

Towards a Conceptual Framework for Artificial Immune Systems

Lecture Notes in Computer Science, 2004

We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of AIS network models. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally hint at the possibility of a novel instantiation of such a metaframework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.

libtissue - a Software System for Incorporating Innate Immunity into Artificial Immune Systems

2000

In a previous paper the authors argue the case for incorporating concepts from innate immunity into Artificial Immune Systems and present an outline for a conceptual frame- work for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed

Innate and Adaptive Principles for an Artificial Immune System

Lecture Notes in Computer Science, 2006

This paper summarises the current literature on immune system function and behaviour, including pattern recognition receptors, danger theory, central and peripheral tolerance, and memory cells. An artificial immune system framework is then presented based on the analogies of these natural system components and a rule and feature-based problem representation. A data set for intrusion detection is used to highlight the principles of the framework.

Advances in artificial immune systems

IEEE Computational Intelligence Magazine, 2006

D uring the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI) as shown in .There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts-computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity.

Theoretical advances in artificial immune systems

2008

D uring the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI) as shown in .There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts-computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity.

Experimenting with innate immunity

2010

In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step -the libtissue system.

Alternative Inspiration For Artificial Immune Systems: Exploiting Cohen’s Cognitive Immune Model

In Silico Immunology, 2007

In this chapter, we highlight the idea that actively investigating alternative theories of the immune system can identify new and novel inspiration for artificial immune systems (AIS). It is clear that there is disagreement amongst immunologists concerning many mechanisms of the immune system, often providing an unclear picture for the engineer trying to exploit immune ideas. In spite of this, the abundence of immune theories can be beneficial to the AIS practioner if investigated in detail. Here we provide an example of this by describing Irun Cohen's cognitive immune model and showing how, from this, new ideas for AIS inspiraction can be identified and exploited.

An interdisciplinary perspective on artificial immune systems

Evolutionary Intelligence, 2008

... Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and ... such as mathematical and computational modelling of immunology, abstraction from those models into algorithm (and system) design and ...