2014/03/01 by Menghan Wang, Wang, Menghan
Computer Science · Engineering · Environmental Science · #Advanced Computational Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Industrial Technology and Control Systems #Water Quality Monitoring and Analysis #cs.AI
paper · pdf · doi:10.48550/arxiv.1403.0036
arxiv created 2014/03/01 · openalex publication_date 2014/03/01 · arxiv updated 2014/03/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
The Distributed Cooperative Modeling System (DCMS) solves complex decision problems involving a lot of participants with different viewpoints by network based distributed modeling and multi-template aggregation. This thesis aims at extending the system with support for dynamic decision making process. First, the thesis presents a discussion of characteristics and optimal policy finding Markov Decision Process as well as a brief introduction to dynamic Bayesian decision network, which is inherently equal to MDP. After that, discussion and implementation of prediction in Markov process for both discrete and continuous random variable are given, as well as several different kinds of correlation analysis among multiple indices which could help decision-makers to realize the interaction of indices and design appropriate policy. Appending history data of Macau industry, as the foundation of extending DCMS, is introduced. Additional works include rearrangement of graphical class hierarchy in DCMS, which in turn allows convenient implementation of curve relation-line, which makes template modeling clearer and friendlier.