The Intelligent Decision Technologies (IDT) International Conference encourages an interchange of research on intelligent systems and intelligent technologies that enhance or improve decision making. The focus of IDT is interdisciplinary and includes research on all aspects of intelligent decision technologies, from fundamental development to real applications.
IDT has the potential to expand their support of decision making in such areas as finance, accounting, marketing, healthcare, medical and diagnostic systems, military decisions, production and operation, networks, traffic management, crisis response, human-machine interfaces, financial and stock market monitoring and prediction, and robotics. Intelligent decision systems implement advances in intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, and genetic algorithms, among others.  Emerging areas of active research include virtual decision environments, social networking, 3D human-machine interfaces, cognitive interfaces, collaborative systems, intelligent web mining, e-commerce, e-learning, e-business, bioinformatics, evolvable systems, virtual humans, and designer drugs.
This volume contains papers from the Fourth KES International Symposium on Intelligent Decision Technologies (KES IDT¿12), hosted by researchers in Nagoya University and other institutions in Japan.  This book contains chapters based on papers selected from a large number of submissions for consideration for the conference from the international community. The volume represents the current leading thought in intelligent decision technologies.

From the content: A Compromise Decision-Making Model to Recover Emergency Logistics Network.- A Descriptor-Based Division Chart Table in Rough Non-deterministic Information Analysis.- A Method of Internet-Analysis by the Tools of Graph Theory.- A Multi-Agent Decision Support System for Path Planning under Reciprocal Constraints.- A New Way to Make Decisions with Paired Comparisons.- A Stackelberg Location on a Network with Fuzzy Random Demand Quantities using Possibility Measure.- A Structural Analysis based on Similarity between Fuzzy Clusters and its Application to Evaluation Data.