| Popis: |
The field of artificial immune system (AIS) is an example of biologically inspired computing that takes its inspiration from various aspects of immunology. Techniques from AIS have been applied in solving many different problems such as classification, optimisation and anomaly detection. However, despite the apparent success of the AIS approach, the unique advantages of AIS over and above other computational intelligence approaches are not clear. In order to address this, AIS practitioners need to carefully consider the application area and design methodologies that they adopt. It has been argued that of increasing importance is the development of a greater understanding of the underlying immunological system that acts as inspiration, as well as the understanding of the problem that need to be solved before proposing the immune-inspired solution to solve the desired problem. This thesis therefore aims to pursue a more principled approach for the development of an AIS, considering the application areas that are suitable based on the underlying biological system under study, as well as the engineering problems that needs to be solved. This directs us to recognise a methodology for developing AIS that integrates several explicit modelling phases to extract the key features of the biological system. An analysis of the immunological literature acknowledges our immune inspiration: granuloma formation, which represents a chronic inflammatory reaction initiated by various infectious and non-infectious agents. Our first step in developing an AIS supported by these properties is to construct an Unified Modelling Language (UML) model agent-based simulation to understand the underlying properties of granuloma formation. Based on the model and simulation, we then investigate the development of granuloma formation, based on the interactions of different signalling mechanisms and the recruitment of different cells in the system. Using the insight gained from these investigation, we construct a design principles to be incorporated into AIS algorithm development. The design principles are then instantiated for a self-healing algorithm for swarm robotic systems, specifically in the case of swarm beacon taxis when there exist failure of robots' energy in the systems. The self-healing algorithm, which is inspired by the granuloma formation of immune systems is then tested in swarm robotics simulation. To conclude, we analyse the process we have pursued to develop our AIS and evaluate the advantages and the disadvantages of the approach that we have taken, showing how a more principled approach with careful consideration the application area can be applied to the design of biologically-inspired algorithms. |
