In the past the classical approaches from control theory, simulation, decision making.... were used.
Then, a multi-agent simulation decision system based on Java-Repast is developed.
Modeling and implementing dynamical systems is a central problem in artificial intelligence, robotics, software agents, simulation, decision and control theory, and many other disciplines.
You'll learn about decision trees for structuring decision problems under uncertainty, Monte Carlo simulation for simulating decision situations with many uncertainties, and optimization for finding the best possible solution in problems with many decision variables and constraints.
Our simulation and decision analysis tool was used to assess the impact of key decisions in the exercises.
We will cover stochastic modeling techniques, statistical principles, simulation, and decision-making under uncertainty.
The model suggests an iterative process of theory, simulation, and decision-making interactions.
Another goal of the course is to extend student skills and understanding of advanced quantitative tools such as Solver optimization algorithms, Monte Carlo simulation, and decision analysis.
As well as covering theoretical concepts, the course will put substantial emphasis in computational implementation of both simulation and decision-making problems.
Using the simulation results, decision makers can easily weigh the costs of the system, the water allocation objectives, and the system risk.
