Niazi / Hussain Cognitive Agent-based Computing-I

A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods
2013
ISBN: 978-94-007-3852-2
Verlag: Springer Netherland
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 55 Seiten, eBook

Reihe: SpringerBriefs in Cognitive Computation

Complex Systems are made up of numerous interacting sub-components. Non-linear interactions of these components or agents give rise to emergent behavior observable at the global scale. Agent-based modeling and simulation is a proven paradigm which has previously been used for effective computational modeling of complex systems in various domains. Because of its popular use across different scientific domains, research in agent-based modeling has primarily been vertical in nature. The goal of this manuscript is to provide a single hands-on guide to developing cognitive agent-based models for the exploration of emergence across various types of complex systems. We present practical ideas and examples for researchers and practitioners for the building of agent-based models using a horizontal approach - applications are demonstrated in a number of exciting domains as diverse as wireless sensors networks, peer-to-peer networks, complex social systems, research networks, epidemiological HIV

Zielgruppe

Research

Autoren/Hrsg.

Niazi, Muaz A

Hussain, Amir

Inhaltsverzeichnis

1. Introduction1.1 About the Agent Concept1.2 A Framework for Complex Adaptive Systems1.3 Modeling CAS1.4 Motivation1.5 Aims and Objectives1.6 Overview of the BriefsReferences  2. A Unified Framework2.1 Overview of the Proposed Framework 2.2 Proposed Framework Levels Formulated in Terms of CAS Study Objectives2.3 Proposed Framework Levels Formulated in Relation to Available Data Types2.4 Overview of the Rest of the Parts2.4.1 Overview of Case Studies2.4.2 Outline of the BriefsReferences 3. Complex Adaptive Systems3.1 Overview3.2 Complex Adaptive Systems (CAS)3.2.1 The Seven Basics of CAS3.2.2 Emergence 3.3 Examples of CAS3.3.1 Natural CAS Example 1: CAS in Plants3.3.2 Natural CAS Example 2: CAS in Social Systems3.3.3 Artificial CAS Example 1: Complex Adaptive Communication Networks 3.3.4 Artificial CAS Example 2: Simulation of Flocking BoidsReferences 4. Modeling CAS4.1 Agent-based Modeling and Agent-based Computing4.1.1 Agent-oriented Programming4.1.2 Multi-agent Oriented Programming4.1.3 Agent-based or Massively Multiagent Modeling4.1.4 Benefits of Agent-based Thinking4.2 A Review of an Agent-based Tool4.2.1 NetLogo Simulation: An Overview4.3 Verification and Validation of Simulation Models4.3.1 Overview4.3.2 Verification and Validation of ABMs4.3.3 Related Work on V&V of ABM4.4 Overview of Communication Network Simulators4.4.1 Simulation of WSNs4.4.2 Simulation of P2P Networks4.4.3 Simulation of Robotic Swarms4.4.4 ABM for Complex Communication Networks Simulation4.5 Complex Network Modeling4.5.1 Complex Network Methods4.5.2 Theoretical Basis4.5.3 Centralities and Other Quantitative Measures4.5.4 Centrality Measures4.5.5 Software Tools for Complex Networks4.6 ConclusionsReferences  Index