X-Machines for Agent-Based Modeling : FLAME Perspectives.

X-Machines for Agent-Based Modeling : FLAME Perspectives.

Saved in:
Bibliographic Details
Main Author: Kiran, Mariam
Format: Electronic eBook
Language:English
Published: Portland : Chapman and Hall/CRC, 2017.
Edition:1st ed.
Series:Chapman and Hall/CRC Computer and Information Science Ser.
Subjects:
Multiagent systems.
Online Access:CONNECT
Table of Contents:
  • Cover
  • Half Title
  • PUBLISHED TITLES
  • Title
  • Copyright
  • Dedication
  • Contents
  • Foreword
  • Preface
  • List of Figures
  • List of Tables
  • FLAME Contributors
  • Chapter 1 Setting the Stage: Complex Systems, Emergence and Evolution
  • 1.1 Complex and Adaptive Systems
  • 1.2 What Is Chaos
  • 1.3 Constructing Artificial Systems
  • 1.4 Importance of Emergence
  • 1.5 Dynamic Systems
  • 1.6 Is There Evolution at Work
  • 1.6.1 Adaptation
  • 1.7 Distributing Intelligence
  • 1.8 Modeling and Simulation
  • 1.8.1 Research Examples
  • Chapter 2 Artificial Agents
  • 2.1 Intelligent Agents
  • 2.1.1 "Can Machines Think
  • 2.2 Engineering Self-Organizing Systems
  • 2.2.1 Bring in the Agents
  • 2.2.2 Characteristics of Agent-Based Models
  • 2.3 Agent-Based Modeling Frameworks
  • 2.4 Adaptive Agent Design
  • 2.5 Mathematical Foundations
  • 2.6 Objects or Agents
  • 2.7 Influence of Other Research Areas on ABM
  • Chapter 3 Designing X-Agents Using FLAME
  • 3.1 FLAME and Its X-Machine Methodology
  • 3.1.1 Transition Functions
  • 3.1.2 Memory and States
  • 3.2 Using Agile Methods to Design Agents
  • 3.2.1 Extension to Extreme Programming
  • 3.3 Overview: FLAME Version 1.0
  • 3.4 Libmboard (FLAME message board library
  • 3.4.1 Compiling and Installing Libmboard
  • 3.4.2 FLAME's Synchronization Points
  • 3.5 FLAME's Missing Functionality
  • Chapter 4 Getting Started with FLAME
  • 4.1 Setting Up FLAME
  • 4.1.1 MinGW
  • 4.1.2 GDB GNU Debugger
  • 4.1.3 Dotty as an Extra Installation
  • 4.2 Messaging Library: Libmboard
  • 4.3 How to Run a Model
  • 4.4 Implementation Details
  • 4.5 Using Grids
  • 4.6 Integrating with More Libraries
  • 4.7 Writing a Model
  • Fox and Rabbit Predator Model
  • 4.7.1 Adding Complexity to Models
  • 4.7.2 XML Model Description File
  • 4.7.3 C Function
  • 4.7.4 Additional Files
  • 4.7.5 0.xml File
  • 4.8 Enhancing the Environment.
  • 4.8.1 Constant Variables
  • 4.8.2 Time Rules
  • Chapter 5 Agents in Social Science
  • 5.1 Sugarscape Model
  • 5.1.1 Evolution from Bottom-Up
  • 5.1.2 Distribution of Wealth
  • 5.1.3 Location Is Important
  • 5.1.4 Find Agents around Me
  • 5.1.5 Handle Multiple 'Eaten' Requests
  • 5.1.6 Change Starting Conditions
  • 5.2 Modeling Social Networks
  • 5.2.1 Set Up a Recurring Function
  • 5.2.2 Assigning Conditions with Functions
  • 5.2.3 Using Dynamic Arrays and Data Structures
  • 5.2.4 Creating Local Dynamic Arrays
  • 5.3 Modeling Pedestrians in Crowds
  • 5.3.1 Calculate Movement toward Other Agents
  • 5.3.2 Entering and Exiting Agents
  • Chapter 6 Agents in Economic Markets and Games
  • 6.1 Perfect Rationality versus Bounded Rationality
  • 6.2 Modeling Multiple Shopper Behaviors
  • 6.3 Learning Firms in a Cournot Model
  • 6.3.1 Genetic Programming with Agents
  • 6.3.2 Filtering Messages in Advance
  • 6.3.3 Comparing Two Data Structures
  • 6.4 A Virtual Mall Model: Labor and Goods Market Combined
  • 6.5 Programming Games
  • 6.5.1 Nash Equilibrium
  • 6.5.2 Evolutionary Game Theory
  • 6.5.3 Evolutionary Stable State
  • 6.5.4 Game Theory versus Evolutionary Game Theory
  • 6.5.5 Continuous Strategies
  • 6.5.6 Red Queen and Equilibrium
  • 6.6 Learning in an Iterated Prisoner's Dilemma Game
  • 6.7 Multi-Agent Systems and Games
  • Chapter 7 Agents in Biology
  • 7.1 Example Models
  • 7.1.1 Molecular Systems Models
  • 7.1.2 Tissue and Organ Models
  • 7.1.3 Ecological Models
  • 7.1.4 Industrial Applications of Agent-Based Modeling with FLAME
  • 7.2 Modeling Epithelial Tissue
  • 7.2.1 Merging with Other Toolkits
  • 7.3 Modeling Drosophila Embryo Development
  • 7.3.1 Stochastic Modeling
  • 7.3.2 Converting to an Agent-Based Model
  • 7.3.3 Find Optimum Model Settings
  • 7.4 Output Files for Analysis
  • 7.5 Modeling Pharaoh's Ants (Monomorium pharaonis.
  • 7.6 Model Drug Delivery for Cancer Treatment
  • 7.6.1 Using Multiple Outputs
  • Chapter 8 Testing Agent Behavior
  • 8.1 Unit and System Testing
  • 8.2 Statistical Testing of Data
  • 8.3 Statistics Testing on Code
  • 8.4 Testing Simulation Durations
  • Chapter 9 FLAME's Future
  • 9.1 FLAME to FLAME GPU
  • 9.1.1 Visualizing Is Easy in FLAME GPU
  • 9.1.2 Utilizing Vector Calculations
  • 9.2 Commercial Applications of FLAME
  • Bibliography
  • Index.

Similar Items