MICAI 2002: Advances in Artificial Intelligence: Second Mexican International Conference on Artificial Intelligence Merida, Yucatan, Mexico, April 22-26, 2002 Proceedings
Carlos A. Coello Coello, Alvaro de Albornoz, Luis E. Sucar, Osvaldo C. Battistutti
Springer, May 3, 2002 - Artificial intelligence - 548 pages
This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mérida, Yucatán, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
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Robotics and Computer Vision
Adapting the Messy Genetic Algorithm for Path Planning
Path Planning Using a SingleQuery Bidirectional Lazy Collision
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actions agent analysis anaphora application approach Artificial Intelligence attributes Bayesian network Berlin Heidelberg 2002 C.A. Coello Coello cells classification cluster collaborative complex components Computer concepts conceptual graphs configuration constraint Data Mining data set database Decision Tree defined described detection developed distribution document domain dynamic environment epistemic logic evaluation example execution extracted function fuzzy Genetic Algorithms goal graph hybrid logic IEEE implemented input interaction interlingua International Keystroke Dynamics knowledge language linear LNAI logic Machine Learning method Mexico MICAI modal multi-agent systems Neural Networks nodes objects obtained ontology operator optimization paper parameters path performance planner problem Proc programming pronouns proposed prostate query represented roadmap robot role rules sample scenario selected semantic similar Simulated Annealing simulation solution space Springer-Verlag Berlin Heidelberg statistical strategy structure subset supervised learning Table task techniques threshold Trepan variables