AI and Swarm: Evolutionary Approach to Emergent Intelligence

Iba, Hitoshi

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商品描述

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.

商品描述(中文翻譯)

本書提供了關於人工智慧和群體智能的理論和實踐知識。它提供了一種基於進化算法的複雜自適應系統方法論,並整合了多種元啟發式方法,例如螞蟻優化、人工蜜蜂群體、粒子群優化等等。這些發展有助於改進人工智慧中的問題解決方法。本書還介紹了群體智能在複雜自適應系統、反應擴散計算和擴散限制聚集等應用中的新興用途。

另一個重點是實際應用。我們提供了來自實際問題的實證例子,並展示了所提出的方法在處理群體機器人、矽交通、圖像理解、Vornoi圖、排隊理論和黏液智能等領域的任務時的成功。每一章節都以問題的背景開始,接著介紹該領域的最新技術,最後進行詳細的討論。此外,本書還詳細描述了基於粒子群優化和人工蜜蜂群體的複雜自適應系統模擬器。這些模擬器以及一些源代碼可以在作者的網站上線上獲得,以便讀者有興趣獲得實際操作經驗。

本書介紹的概念旨在促進和促進群體智能方法在理論和實踐中的有效研究。對於其他讀者來說,本書也具有價值,因為它涵蓋了跨學科研究主題,包括人工智慧、複雜自適應系統和元啟發式方法的問題解決任務。

作者簡介

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.

作者簡介(中文翻譯)

Hitoshi Iba是東京大學資訊科學與技術研究所的教授。從1990年到1998年,他在日本茨城的電氣技術研究所(ETL)擔任高級研究員。他是《遺傳編程和可演化機器》(GPEM)期刊的副編輯。他還是一位水下自然學家和有豐富經驗的PADI潛水教練,已完成超過一千次的潛水。