Information Fusion in the Immune System

The field of artificial immune systems (AISs) is an emerging biologically-inspired method which builds systems based on algorithms inspired by the biological immune system. AIS research has provided a number of general purpose techniques and algorithms which have successfully been applied to a range of optimisation, classification and data mining problems. As with evolutionary algorithms and neural networks, AISs could also provide useful solutions to optimisation and classification problems in multi-sensor data fusion. More interestingly though perhaps, recent research in AISs [14,15,35,36] shows the importance of multilevel information in the construction of AISs. New models for AISs are emerging that are inspired by research in immunology into the role of the innate immune system in overall immune system dynamics. These AISs, which incorporate mechanisms inspired by both the innate and adaptive immune systems, are called second generation AISs. They stand in contrast to first generation AISs, which are inspired by adaptive immune system mechanisms only. One of the consequences of incorporating innate and adaptive mechanisms, as well as one of the defining characteristics of second generation AISs, is the need for a multilevel problem representation, and a multi-le- vel interaction of the components of the AIS with the problem [36]. As systems that integrate multilevel information sources, second generation AISs share much in common with multi-sensor data fusion systems.