Issue No.12 - December (2004 vol.37)
Published by the IEEE Computer Society
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2004.234
AI Game Engine Programming, Brian Schwab. This book provides the tools and background that game developers need to create modern game AI engines. It takes programmers from theory to actual game development, with usable code frameworks designed to go beyond merely detailing how a technique might be used. The author focuses on helping those developers who struggle to determine which techniques to use or which working code would best suit a particular game.
The book surveys the capabilities of different AI approaches; reviews the techniques used in some current AI engines; and covers common pitfalls, design considerations, and optimizations across a variety of game genres. It also provides a clean and usable interface for several game AI techniques, with an emphasis on primary decision-making paradigms.
The book concludes with a look at game AI development in the real world, focusing on how distributed AI works as an overall paradigm that can help with the organization of any AI engine.
Charles River Media; www.charlesriver.com; 1-58450-344-0; 594 pp.; $49.95.
Economics of Information Security Series: Advances in Information Security, vol. 12, L. Jean Camp and Stephen Lewis, eds. This book applies economics to the analysis and understanding of security problems. It examines security, privacy, and trusted computing distinctly and as elements of a larger dynamic system.
Although designed for managers struggling to understand the risks in organizations dependent on secure networks, this book can also benefit researchers and students of computer science, policy, and management.
Kluwer; www.wkap.nl; 320 pp.; 1-4020-8089-1; $120.00.
Agile Software Development in the Large: Diving Into the Deep, Jutta Eckstein. By embracing requirement changes late in the project and focusing on the human factor in software development, agile processes can react flexibly to continuously changing requirements. Unfortunately, most agile processes have been developed to support small to midsize software development projects, which ill suits large teams that must deal with speedy changes in requirements.
This book shows how those frustrated with static, inflexible processes unsuited to large projects can harness the efficiency and adaptability of agile processes. Topics covered include how the principles and value system of agile processes affect large teams, the impact on a team that switches to an agile process, and how team and project size can influence the underlying architecture.
Dorset House; www.dorsethouse.com; 248 pp.; 0-932633-57-9; $39.95.
Requirements Engineering, 2nd ed., Elizabeth Hull, Kenneth Jackson, and Jeremy Dick. Written for those seeking to develop their knowledge of the requirements engineering process, this book combines the latest research with practical industry experience to guide practitioners in writing and structuring requirements. It explains the importance of systems engineering and the creation of effective solutions to problems.
The book also details the underlying representations used in system modeling, considers the relationship between requirements and modeling, covers a generic multilayer requirements process, and discusses the key elements of effective requirements management.
Springer; www.springeronline.com; 198 pp.; 1-85233-879-2; $69.95.
Pattern Recognition Algorithms for Data Mining, Sankar K. Pal and Pabitra Mitra. This book addresses different pattern recognition tasks in a unified framework that offers both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering and classification, and rule generation and evaluation.
This volume uses both classical approaches and hybrid paradigms to present various theories, methodologies, and algorithms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.
The book also describes methodologies for multiscale data condensation and unsupervised dimensionality reduction for large data; presents active learning strategies for handling a large quadratic problem in an SVM framework; describes design procedures for a rough self-organizing map; applies fuzzy sets, rough sets, neural nets, and genetic algorithms to classification problems, rule generation, and evaluation in a supervised mode; and provides experimental results on real-life data.
Chapman & Hall/CRC; www.crcpress.com; 1-58488-457-6; 280 pp.; $89.95.