1.4 Background
1.4.2 Artificial Immune Systems
Artificial Immune Systems (AIS) are those bio-inspired algorithms that derive their computational intelligence by mimicking the complex processes that constitute a Biological Immune System (BIS).
The Biological Immune System (BIS) comprises a set of defense mechanisms
1. INTRODUCTION
found only in vertebrates. Several biological processes protect the host against a large spectrum of invading antigens [25]. The primary aim of the immune system is to identify the self (host cells) and non-self cells (antigens) in the body and eliminate the latter. In order to do so, the immune system produces a range of cells such as B-cells, Cytotoxic T-cells, Helper T-cells, etc. These cells, in turn, secrete proteins known as antibodies. The primary function of these antibodies is to identify the antigens, tag and kill them and eventually aid their removal from the body. The recognition of the antigen by the antibody is based on the binding between the two. The extent of this binding is called affinity. More the binding, the better is the affinity. The binding site on the antibody is called the paratope and the complementary area on the antigen is called the epitope. While a specific antibody has the same type of paratopes, an antigen can have differently shaped epitopes (as shown in Fig. 1.1a). Antibodies that recognize an antigen (that is, when the paratopes are complementary to an epitope of the antigen) get activated.
This behavior is illustrated in the figure 1.1b.
Various immune theories have been proposed in the domain of AIS, the promi- nent ones being Clonal selection, Danger theory, and Immune Network theory. The Clonal selection theory [2] states that whenever any antibody recognizes an antigen, a clonal expansion occurs, leading to an increase in its population. Somatic hy- permutation [69] increases the average affinities of these clones towards the antigen that triggered the cloning. Clonal expansion also leads to the creation of memory cells, [125] which hold the information of the antigen to utilize them in the future, in the case of a similar attack. Danger theory [84] postulates that when a cell dies an unnatural death, it sends out danger signals and establishes a danger zone around itself [4]. When the antibodies sense these signals, they are attracted towards this zone where they capture and kill the antigens. Those having more affinity undergo clonal expansion. This increase in the population of antibodies that are better suited to the situation aids in quickly curtailing the growth of the antigens. The Immune network theory [61], on the other hand, propounds that antibodies on their own form a network even in the absence of an antigen. This network is called the Immune network. This network is formed based on stimulations and suppressions amongst the antibodies and their respective concentrations. Farmer et al. [36] designed the
1.4. BACKGROUND
equations which model these interactions between the antibodies and the invading antigen.
AIS-based learning exhibits the Lifelong learning phenomenon, a mechanism that not only improves the learning over time but also adapts and learns to adapt to the new conditions while also remembering how the antigens were effectively tackled.
The antibodies of a BIS can learn to eliminate new invading antigens, remember the mode of tackling so that, in future if the same attack occurs, it can be quickly quelled. Adaptation to contain new antigenic attacks and a memory for each such attack, makes the BIS a perfect inspiration for achieving lifelong learning.
From a computational perspective, a population of antibodies is equivalent to a population of solutions in an EA. The respective affinities of the antibodies are synonymous to the fitness values of the solutions in an EA. It may be noted that, unlike in an EA, the antibodies can clone based on their affinities towards the antigens, thereby creating sub-populations of heterogeneous antibodies. An extensive survey of AIS has been reported by Dasgupta et al. [29, 27, 28]. AIS based algorithms have been applied in various areas of Computer Science such as computer security [44, 43, 68], data mining [52], optimization [53, 121], anomaly detection [48], pattern recognition [79], etc. AIS has also been widely used in the field of robotics [60, 114, 129, 94, 105, 104]. Hart, et al. have [56] reported that the majority of publications on AIS have been on benchmark problem instances rather than real-world problems. The authors have also reported that AIS-based applications in robotics are mostly simulated in artificial environments. The need to envisage and implement AIS based applications in physical environments is thus, imperative.
Combining EA and AIS for Learning Robot Controllers
The nature of the EA, the evaluation-selection-variation process, ensures that every condition encountered by the robot and the robot’s corresponding action is taken into account and evaluated so as to aid the learning of the robot controllers. This thesis focuses on EA-based learning of robot controllers. However, as explained earlier, the EA is prone to loss of learned information in the past, during the evolu- tionary process. On the other hand, an AIS is a much more promising technique to
1. INTRODUCTION
adapt to dynamic conditions without the loss of such learned information. Such a feature, if incorporated within a robot’s controller, will drastically ameliorate its per- formance especially when the environmental conditions are complex and dynamic.
This work reported in this thesis, thus describes such an integration of AIS and EA based techniques, that culminates in an apt recipe for lifelong learning in the domain of robotics.