Computer based simulation systems Syllabus

Instructor

Mai Tiến Dũng

Course Description

Computer based simulation is a technique that has gained widespread use and is of fundamental importance in the design and evaluation
of many types of systems. Due to the inherent complexity of systems in today’s world, tractable analytical solutions often are found
lacking in precision or flexibility in the face of changing conditions, and this is where simulation plays an important role.

This course emphasizes the development of modeling and simulation concepts and analysis skills necessary to design, program, implement,
and use computers to solve complex systems/products analysis problems, from national air traffic, manufacturing and business processes,
to telecommunications and computer systems. The key emphasis is on problem formulation, model building, data analysis, solution
techniques, and evaluation of alternative designs/processes in complex systems/products. Overview of modeling techniques and methods
used in decision analysis, including Monte Carlo simulation and systems dynamics modeling are presented.

The focus of this lecture will be theoretical aspects of modeling and simulation and real-world practices primarily towards the defense
industry and game programming; which includes earth modeling, entity modeling, behavior modeling, sensor & weapon systems modeling,
distributed simulations, simulation based optimization and analysis.

Course Objectives

To provide the students with the following capabilities:

· To apply modern software packages to conduct analysis of real world data.

· To understand the technical underpinning of modern computer simulation software.

· The ability to apply the appropriate analytical technique to a wide variety of real world problems and data sets.

· To summarize and present the analysis results in a clear and coherent manner.

• To develop your own applications.

· To learn about some real-word practices in the context of defense industry and game programming.

Course Grade

20% Mid-term exam·

20% Homework

30% Projects

30% Final exam·

Course Outline

1.
Introduction To Modeling & Simulation

– What is Modeling and Simulation?

– Complexity Types

– Model Types

– Simulation Types

– M&S Terms and Definitions

2.
Input Data Analysis

– Simulation Input Modeling

– Input Data Collection

1. Data Collection Problems

2. Practical Suggestions

3. Effect of Period of Time

– Input Modeling Strategy

1. Histograms

2. Probability Distributions

3. Selecting a Probability Distribution

4. Evaluating Goodness of Fit

3.
Random Variate Generation

– Random Numbers

– Random Number Generators

– Random Variate Generation

1. Factors to be considered

2. General principles

1. Inverse Transform Method

2. Acceptance-Rejection Method

3. Composition Method

4. Relocate and Rescale Method

3. Specific distributions

4.
Output Data Analysis

– Introduction

1. Types of Simulation With Respect to Output Analysis

2. Stochastic Process and Sample Path

3. Sampling and Systematic Errors

4. Mean, Standard Deviation and Confidence Interval

– Analysis of Finite-Horizon Simulations

1. Single Run

2. Independent Replications

3. Sequential Estimation

– Analysis of Steady-State Simulations

1. Removal of Initialization Bias (Warm-up Interval)

2. Replication-Deletion Approach

3. Batch-Means Method

5.
Comparing Systems via Simulation

– Introduction

– Comparison Problems

1. Comparing Two Systems

2. Screening Problems

3. Selecting the Best

4. Comparison with a Standard

5. Comparison with a Fixed Performance

6.
Discrete Event Simulations

– Introduction

1. Next-Event Time Advance

2. Arithmetic and Logical Relationships

3. Discrete-Event Modeling Approaches

– Event-Scheduling Approach

– Process-Interaction Approach

– Processes and Resources

7.
A Discrete Event Simulation Language: General Purpose Simulation System (GPPS)

– Simulation Languages

– GPSS Simulation Language

– A Sample Simulation Model

8.
A Distributed Simulation Standard: High Level Architecture (HLA)

– Introduction to Distributed Simulations

– Introduction to HLA

– HLA Interface Specification:

1. Federation Management

2. Declaration Management

3. Object Management

– Conclusion

9.
Entity Behavior Modeling

– Introduction to Artificial Intelligence (AI)

– AI Techniques & Architectures

– Some AI Algorithms/Techniques

1. Finite State Machines

2. Decision Trees

3. Artificial Neural Networks

4. Logic Programming

5. Production Systems

6. Genetic Algorithms

7. Path Planning

8. Script Programming

– Conclusion

Project and Homeworks

The topics proposed below will be towards the development of an experimental game environment

1) Environment modeling

1.1) Waterfall modeling

1.2) Water surface modeling

1.3) Grassland modeling

1.4) Scrubland modeling

1.5) Tree modeling

1.5.1) Needle leaf tree modeling

1.5.2) Broad leaf tree modeling

1.5.3) Leafless tree modeling

1.6) 3D game world modeling

2) Behavior modeling

2.1) Fish behavior modeling

2.2) Bird behavior modeling

2.3) Land animal behavior modeling

2.4) Attack behavior modeling

2.5) Defense behavior modeling

2.6) Head/looking direction modeling

3) Body movement (bone structure) modeling

3.1) Human body movement modeling

3.2) Land animal body movement modeling

4) Effects modeling (wind effect integrated)

4.1) Particle effect modeling (smoke, flame, explosion, missile trail)

4.2) Rain and snow modeling

4.3) Volumetric cloud modeling

5) Physics modeling

5.1) Rigid body collision detection and avoidance

5.2) Parachute modeling

6) Distributed simulation

6.1) Multi player land vehicle modeling with HLA

6.2) Massive multi player role playing game environment modeling

7) Sensor Modeling

7.1) Night vision goggles modeling

7.2) Analysis of sensor detection, recognition and identification performance

8) Sound programming

8.1) 3D sound effects modeling

Course Requirements

The students are expected to develop a small-scale project with a team of preferably at most three people. The project will include
documentation, implementation and a class presentation. The project topic can be either proposed by the team or selected from a list of
topics provided by the lecturer, which will be towards the development of an experimental game environment on Windows platform.

References

1. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice”, John Wiley & Sons, Inc.,
1998.

2. George S. Fishman, “Discrete-Event Simulation: Modeling, Programming and Analysis”, Springer-Verlag New York, Inc., 2001.

3. Andrew F. Seila, Vlatko Ceric, Pandu Tadikamalla, “Applied Simulation Modeling”, Thomson Learning Inc., 2003.

4. Banks, Carson, Nelson, Nicol , Discrete Event System Simulation, 3rd edition, , Prentice Hall, 2001, ISBN 0130887021

5. Richard M. Fujimoto , Parallel and Distributed Simulation Systems, 1st edition, Wiley-Interscience, 2000, ISBN 0471183830