It is inspired by the main mechanism in the thymus that produces a set of mature T-cells capable of binding only non-self antigens. The starting point of this algorithm is to produce a set of self strings, S, that define the normal state of the system. The task then is to generate a set of detectors, D, that only bind/recognize the complement of S. These detectors can then be applied to new data in order to classify them as being self or non-self, thus in the case of the original work by Forrest, highlighting the fact that data has been manipulated
Published in Chapter:
Applications of Artificial Immune Systems in Agents
Luis Fernando Niño Vasquez (National University of Colombia, Colombia), Fredy Fernando Muñoz Mopan (National University of Colombia, Colombia), Camilo Eduardo Prieto Salazar (National University of Colombia, Colombia), and José Guillermo Guarnizo Marín (National University of Colombia, Colombia)
Copyright: © 2009
|Pages: 24
DOI: 10.4018/978-1-60566-310-4.ch005
Abstract
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high efficacy. This is a survey chapter within which single and multi-agent systems inspired by immunology concepts are presented and analyzed. Most of the work is usually based on the adaptive immune response characteristics, such as clonal selection, idiotypic networks, and negative selection. However, the innate immune response has been neglected and there is not much work where innate metaphors are used as inspiration source to develop robotic systems. Therefore, a work that involves some interesting features of the innate and adaptive immune responses in a cognitive model for object transportation is presented at the end of this chapter.