This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards better problem-solving methodologies in AI. The book also covers emerging uses of swarm intelligence in applications such as complex adaptive systems, reaction-diffusion computing, and diffusion-limited aggregation, etc.
Another emphasis is its real-world applications. We give empirical examples from real-world problems and show that the proposed approaches are successful when addressing tasks from such areas as swarm robotics, silicon traffics, image understanding, Vornoi diagrams, queuing theory, and slime intelligence, etc.
Each chapter begins with the background of the problem followed by the current state-of-the-art techniques of the field, and ends with a detailed discussion. In addition, the simulators, based on optimizers such as PSO and ABC complex adaptive system simulation, are described in detail. These simulators, as well as some source codes, are available online on the author’s website for the benefit of readers interested in getting some hands-on experience of the subject.
The concepts presented in this book aim to promote and facilitate the effective research in swarm intelligence approaches in both theory and practice. This book would also be of value to other readers because it covers interdisciplinary research topics that encompass problem-solving tasks in AI, complex adaptive systems, and meta-heuristics.
Table of Contents
What is AI? – Strong AI VS Weak AI
What is emergence?
Cellular automaton and edge of chaos
AI, Alife and Emergent computation
How to make a bit? – Exploration vs. exploitation
Wire world: A computer implemented as a cellular automation
Ant colony optimization (ACO)
Particle swarm optimization (PSO)
Artificial Bee Colony optimization (ABC)
Harmony search (HS)
Cat swarm optimization (CSO)
Emergent Properties and swarm intelligence
Queuing theory and traffic jams
Silicon Traffic and Rule 184
Segregation and immigration: What is right?
Complex adaptive systems
Diffusion-Limited Aggregation (DLA)
How do snowflakes form?
Why do fish patterns change?
BZ reaction and its oscillation
Why do we have mottled snakes? Theory of Murray
Emergence of intelligence
Evolution of cooperation and defection
Evolutionary psychology and mind theory
How does slime solve a maze problem? Slime intelligence
Hitoshi Iba is a Professor at the Graduate School of Information Science and Technology at the University of Tokyo. From 1990 to 1998, he was a senior researcher at the Electro Technical Laboratory (ETL) in Ibaraki, Japan. He is an Associate Editor of the Journal of Genetic Programming and Evolvable Machines (GPEM). He is also is an underwater naturalist and experienced PADI divemaster having completed more than a thousand dives.