INTRODUCTION TO IIDLE The Immunological Inspired Distributed Learning Environment

The mammalian acquired immune system is a robust and powerful information processing system that demonstrates features such as decentralised control, parallel processing, adaptation, and learning by experience. Artificial immune systems (AIS) are machine-learning algorithms that are imbued with some of the principles, and attempt to take advantage of some benefits of natural immune systems for use in tackling difficult problem domains. The clonal selection principle (or clonal expansion principle) is a theory used to describe the basic properties of the acquired immune system. It is the idea that only those cells that are activated by external stimuli proliferate and differentiate (or are selected), and those that are not activated are selected against. The Immunological Inspired Distributed Learning Environment (IIDLE) is an artificial immune system technique that is inspired both by the clonal selection theory for learning in the acquired immune system, as well as the spatially distributed and circulatory nature of the system. An introductory overview of the IIDLE is provided, highlighting both the inspiration and conceptualisation of the system, as well as speculating as to the expected benefits and applicability of the novel technique.