Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

Abstract

Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
by D. Floreano and C. Mattiussi

This is a book that bridges biological systems and computer science. For digital-based researchers, having this book which details the biological components of natural life and seamlessly integrates that knowledge into our digital realm is an essential asset. Each chapter is systematically introduces the reader to a biological system while easing them into the its computational counterpart. There are seven chapters covering evolution, cellular, neural, developmental, immune, behavioral, and collective systems.

Chapter 1 introduces the fundamental concept of computational evolution as related to biological systems. This chapter starts with the basic concepts of evolutionary theory and progresses, covering everything from fitness functions to analog circuits. The following chapter presents the next logical step upwards in biology, cellular structures and systems. Again introducing the basics of life and progressing towards cellular automata.  Chapter 3 covers Neural Networks by introducing the Biological Nervous System, then the Artificial Neural Network. The core concepts to Neural Networks are detailed in a systematic and common-sense manner, introducing unsupervised learning, supervised learning, and reinforce learning, then progressing onto neural hardware and hybrid systems. In Chapter 4, the authors detail developmental systems, explaining how nature utilizes the cellular structures to how engineers can mimic nature. This theme of progression from biological introduction to digital computation is reproduced as a single voice through out each chapter. The fundamentals of Bio-Inspired Artificial Intelligence are well demonstrated, allowing for a novice researcher in this area to develop the necessary skills and have a firm grasp on this topic.

Once the reader has a solid grasp of the building blocks of life, the authors present chapters related to larger systems. Of particular interest to my research is the chapter on Immune Systems. This chapter provides a fundamental understanding of the Human Immune System, detailing the finer points of immunological cellular structures, while introducing a slightly more than generalized immune response concept. After a lengthy introduction of human immunology, we are introduced to the core of Artificial Immune Systems, the Negative Selection Algorithm and Clonal Selection Algorithm. Each one of these algorithms is covered enough so that the reader is capable of understanding each respective algorithms strengths and limitations. For new researchers to Artificial Immune Systems, days of reading journal articles is summarized in these sections, allowing for intelligent and efficient decision making in choosing your next step of research.

Chapter 6 and 7 provides the audience with behavior systems and collective systems, respectively. The behavioral systems covered in this book relate to aspects of AI, robots, and some machine learning. Once behavior is understood, collective and cooperative systems are covered. Optimization techniques of particle swarms, ant colonies, and topics derived for robotics are detailed and well explained.

While this is not a textbook, is does cover the fundamental concepts required to research Bio-Inspired Artificial Intelligence. For myself, the quality of this book can simply be noted by the publishers, MIT Press. Many of the best books I have encountered in my studies have been published by MIT, and here is another. Floreano and Mattiussi have not let me down in their quality, albeit I do have some complaints.

First, while the topics cover a solid breadth, the depth on detailing the computation side is limited. I would like to have seen either more depth in each chapter or a broader look at each chapters algorithms, but the book falls somewhere in the middle. My current research involves Danger Signals and their relationship to preventing Epidemic Attacks, so I would have like to seen more detail about Polly Matzinger's Danger Theory rather than one short paragraph saying that it is not universally accepted. While Immunologists may debate Danger Theory, novel algorithms have been developed off of the concept of Danger Theory and deserve a place in this book. Yet to counter my own argument, the authors do finish off each chapter with a Suggested Readings section outlining a series of excellent supplement papers to the chapters topics that would eventually lead the reader to these novel topics.

Overall, if you are interested in this field, buy this book. You can find it online at MIT Press for a discounted price. This book will make an excellent addition to any computer researchers library.

Anthony Kulis,
Department of Computer Science, Southern Illinois University