LiU Electronic Press
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Author:
Olsson, Per-Magnus (Linköping University, The Institute of Technology) (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, Department of Mathematics, Optimization )
Title:
Practical Pathfinding in Dynamic Environments
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, Department of Mathematics, Optimization
Linköping University, The Institute of Technology
Publication type:
Chapter in book (Other academic)
Language:
English
In:
AI Game Programming Wisdom 4
Editor:
Steve Rabin
Edition:
1
Place of publ.: Boston Publisher: Charles River
Year of publ.:
2008
URI:
urn:nbn:se:liu:diva-40998
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-40998
ISBN:
978-1-58450-523-5, 158-450-523-0
Local ID:
54873
Subject category:
Computer Science
SVEP category:
Computer science
Abstract(en) :

Welcome to the latest volume of AI Game Programming Wisdom! AI Game Programming Wisdom 4 includes a collection of more than 50 new articles featuring cutting-edge techniques, algorithms, and architectures written by industry professionals for use in commercial game development. Organized into 7 sections, this comprehensive volume explores every important aspect of AI programming to help you develop and expand your own personal AI toolbox. You'll find ready-to-use ideas, algorithms, and code in all key AI areas including general wisdom, scripting and dialogue, movement and pathfinding, architecture, tactics and planning, genre specific, and learning and adaptation. New to this volume are articles on recent advances in realistic agent, squad, and vehicle movement, as well as dynamically changing terrain, as exemplified in such popular games as Company of Heroes.You'll also find information on planning as a key game architecture, as well as important new advances in learning algorithms and player modeling. AI Game Programming Wisdom 4 features coverage of multiprocessor architectures, Bayesian networks, planning architectures, conversational AI, reinforcement learning, and player modeling.These valuable and innovative insights and issues offer the possibility of new game AI experiences and will undoubtedly contribute to taking the games of tomorrow to the next level.

Available from:
2009-10-10
Created:
2009-10-10
Last updated:
2013-07-05
Statistics:
54 hits