Immunology as a Metaphor for Computational ... - Napier University

Chapter 2. Background 35Immunos 81 embodies the biological concepts of T-Cells (in contrast to most AISmodels reviewed in this chapter), B-Cells, learning, and recognition. T-Cells are usedto control the production of B-Cells within the system, and learn the primary structureof antigens, that is in this case the type and sequence of variables in the antigen data. B-Cells in the system perform ’instance recognition’, i.e. they recognise specific featuresof each individual antigen. The system does not explicitly represent a concept of self,neither does it simulate idiotypic interactions between B-Cells. Furthermore, it doesnot contain any pre-formed T or B-Cells, they are created as the system runs.The system was tested on two standard machine-learning data-sets, consisting ofeight nominal and six continuous variables. The first data set known as the Clevelanddata-set consisted of 303 records from patients with suspected coronary artery disease.This was used as a training set prior to presenting the system with 200 unknown patientcases. Correct classification of the unknown data was produced 80¦ in 3% of cases,and when cross-validation runs on the original Cleveland data set were performed,performance ranged from a high of 96% to a low 63¦ of 2%. These figures comparedvery well with other machine learning techniques, the closest competitor being a k-nearest neighbour classifier.2.5 Immune Algorithms for SchedulingAs stated in the introductory chapter, one of the themes of this thesis is to explorethe use of an immune metaphor in the context of scheduling. An extensive searchof the literature revealed some other applications of artificial systems to this broaddomain. [Fukuda et al., 1999, Mori et al., 1997] describe a general framework for anautonomous distributed system to control semiconductor production. Their systemconsisted of multiple agents, corresponding to features of the immune system, to controlproduction. Thus, the framework consisted of four types of agent, detector agents,mediator agents, inhibitor agents and restoration agents, which interacted with eachother and with a production line, Detector agents,corresponding to B-Cells in the immunesystem, were used to detect specific malfunctions in the system. Mediator agentsmimic the behaviour of lymphokines which are secreted by T-Cells in the immune sys-