【正文】 attern recognition, named Artificial Immune Systems (AIS). AIS take inspiration from the immune system in order to build novel putational tools to solve problems in a vast range of domain areas. The basic immune theories used to explain how the immune system perform pattern recognition are described and their corresponding putational models are presented. This is followed with a survey from the literature of AIS applied to pattern recognition. The chapter is concluded with a tradeoff between AIS and artificial neural networks as pattern recognition paradigms.Keywords: Artificial Immune Systems；Negative Selection；Clonal Selection；Immune Network1 IntroductionThe vertebrate immune system (IS) is one of the most intricate bodily systems and its plexity is sometimes pared to that of the brain. With the advances in the biology and molecular genetics, the prehension of how the immune system behaves is increasing very rapidly. The knowledge about the IS functioning has unraveled several of its main operative mechanisms. These mechanisms have demonstrated to be very interesting not only from a biological standpoint, but also under a putational perspective. Similarly to the way the nervous system inspired the development of artificial neural networks (ANN), the immune system has now led to the emergence of artificial immune systems (AIS) as a novel putational intelligence paradigm. Artificial immune systems can be defined as abstract or metaphorical putational systems developed using ideas, theories, and ponents, extracted from the immune system. Most AIS aim at solving plex putational or engineering problems, such as pattern recognition, elimination, and optimization. This is a crucial distinction between AIS and theoretical immune system models. While the former is devoted primarily to puting, the latter is focused on the modeling of the IS in order to understand its behavior, so that contributions can be made to the biological sciences. It is not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the IS have contributed to the development of AIS. This chapter is organized as follows. Section 2 describes relevant immune theories for pattern recognition and introduces their putational counterparts. In Section 3, we briefly describe how to model pattern recognition in artificial immune systems, and present a simple illustrative example. Section 4 contains a survey of AIS for pattern recognition, and Section 5 contrast the use of AIS with the use of ANN when applied to pattern recognition tasks. The chapter is concluded in Section 6.2 Biological and Artificial Immune SystemsAll living organisms are capable of presenting some type of defense against foreign attack. The evolution of species that resulted in the emergence of the vertebrates also led to the evolution of the immune system of this species. The vertebrate immune system is particularly interesting due to its several putational capabilities, as will be discussed throughout this section. The immune system of vertebrates is posed of a great variety of molecules, cells, and organs spread all over the body. There is no central organ controlling the functioning of the immune system, and there are several elements in transit and in different partments performing plementary roles. The main task of the immune system is to survey the organism in the search for malfunctioning cells from their own body (., cancer and tumour cells), and foreign disease causing elements (., viruses and bacteria). Every element that can be recognized by the immune system is called an antigen (Ag). The cells that originally belong to our body and are harmless to its functioning are termed self (or self antigens), while the disease causing elements are named nonself (or nonself antigens). The immune system, thus, has to be capable of distinguishing between what is self from what is nonself。 a process called self/nonself discrimination, and performed basically through pattern recognition events. From a pattern recognition perspective, the most appealing characteristic of the IS is the presence of receptor molecules, on the surface of immune cells, capable of recognising an almost limitless range of antigenic patterns. One can identify two major groups of immune cells, known as Bcells and Tcells. These two types of cells are rather similar, but differ with relation to how they recognise antigens and by their functional roles. Bcells are capable of recognising antigens free in solution (., in the blood stream), while Tcells require antigens to be presented by other accessory cells.Antigenic recognition is the first prerequisite for the immune system to be activated and to mount an immune response. The recognition has to satisfy some criteria. First, the cell receptor recognises an antigen with a certain affinity, and a binding between the receptor and the antigen occurs with strength proportional to this affinity. If the affinity is greater than a given threshold, named affinity threshold, then the immune system is activated. The nature of antigen, type of recognising cell, and the recognition site also influence the oute of an encounter between an antigen and a cell receptor.The human immune system contains an organ called thymus that is located behind the breastbone, which performs a crucial role in the maturation of Tcells. After Tcells are generated, they migrate into the thymus where they mature. During this maturation, all Tcells that recognise selfantigens are excluded from the population of Tcells。 a process named clonal selection. In contrast, if a Bcell recognises a selfantigen, it might result in suppression, as proposed by the immune network theory. In the following subsections, each of these processes (negative selection, clonal selection, and network theory) will be described separately, along with their putational algorithms counterparts. Negative SelectionThe thymus is responsible