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Hands-On Simulation Modeling with Python

You're reading from   Hands-On Simulation Modeling with Python Develop simulation models for improved efficiency and precision in the decision-making process

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804616888
Length 460 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with Numerical Simulation
2. Chapter 1: Introducing Simulation Models FREE CHAPTER 3. Chapter 2: Understanding Randomness and Random Numbers 4. Chapter 3: Probability and Data Generation Processes 5. Part 2:Simulation Modeling Algorithms and Techniques
6. Chapter 4: Exploring Monte Carlo Simulations 7. Chapter 5: Simulation-Based Markov Decision Processes 8. Chapter 6: Resampling Methods 9. Chapter 7: Using Simulation to Improve and Optimize Systems 10. Chapter 8: Introducing Evolutionary Systems 11. Part 3:Simulation Applications to Solve Real-World Problems
12. Chapter 9: Using Simulation Models for Financial Engineering 13. Chapter 10: Simulating Physical Phenomena Using Neural Networks 14. Chapter 11: Modeling and Simulation for Project Management 15. Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems 16. Chapter 13: What’s Next? 17. Index 18. Other Books You May Enjoy

Multi-agent simulation

An agent can be defined as anything that is able to perceive an environment through sensors and act in it through actuators. Artificial intelligence focuses on the concept of a rational agent, or an agent who always tries to optimize an appropriate performance measure. A rational agent can be a human agent, a robotic agent, or a software agent. In the following diagram, we can see the interaction between the agent and the environment:

Figure 5.9 – Interaction between the agent and the environment

An agent is considered autonomous when it can flexibly and independently choose the actions to be taken to achieve its goals without constantly resorting to the intervention of an external decision system. Note that, in most complex domains, an agent can only partially obtain information and have control in the environment that it has been inserted into, thus exerting, at most, a certain influence on it.

An agent can be considered autonomous...

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