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Record Nr. |
UNINA9910437823103321 |
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Autore |
Niazi Muaz A |
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Titolo |
Cognitive Agent-based Computing-I [[electronic resource] ] : A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods / / by Muaz A Niazi, Amir Hussain |
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Pubbl/distr/stampa |
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Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 |
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ISBN |
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1-283-74213-6 |
94-007-3852-8 |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (65 p.) |
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Collana |
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SpringerBriefs in Cognitive Computation, , 2212-6023 |
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Disciplina |
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Soggetti |
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Neurosciences |
Computer science |
Mathematics |
Cognitive psychology |
Biophysics |
Biological physics |
Computer Science, general |
Mathematics, general |
Cognitive Psychology |
Biological and Medical Physics, Biophysics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cognitive Agent-basedComputing-I; Acknowledgments; Contents; Acronyms; Abstract; 1 Introduction; 1.1...About the AgentAgent Concept; 1.2...A Framework for Complex Adaptive Systems; 1.3...Modeling CASCAS; 1.4...Motivation; 1.5...Aims and Objectives; 1.6...Overview of the Briefs; References; 2 A Unified Framework; 2.1...Overview of the Proposed Framework; 2.2...Proposed Framework Levels Formulated in Terms of CASCAS Study Objectives; 2.3...Proposed Framework Levels Formulated in Relation to Available Data Types; 2.4...Overview of the Rest of the Parts; 2.4.1 Overview of Case Studies; 2.4.2 Outline of the Briefs |
References3 Complex Adaptive Systems; 3.1...Overview; 3.2...Complex |
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Adaptive Systems (CASCAS); 3.2.1 The Seven Basics of CASCAS; 3.2.2 Emergence; 3.3...Examples of CASCAS; 3.3.1 Natural CASCAS Example 1: CAS in Plants; 3.3.2 Natural CASCAS Example 2: CAS in Social Systems; 3.3.3 Artificial CASCAS Example 1: Complex Adaptive Communication Networks; 3.3.4 Artificial CASCAS Example 2: Simulation of Flocking Boids; References; 4 Modeling CASCAS; 4.1...AgentAgent-based Modeling and Agent-based Computing; 4.1.1 AgentAgent-oriented ProgrammingAgentAgent-Oriented Programming |
4.1.2 Multi-agentagent Oriented Programming4.1.3 AgentAgent-based or Massively Multiagent Modeling; 4.1.4 Benefits of AgentAgent-based Thinking; 4.2...A Review of an AgentAgent-based Tool; 4.2.1 NetLogo Simulation: An Overview; 4.2.1.1 Overview of NetLogo for Modeling Complex Interaction ProtocolsOverview of NetLogo for Modeling Complex Interaction Protocols; 4.2.1.2 Capabilities in Handling a Range of Input Values; 4.2.1.3 Range of Statistics and Complex Metrics; 4.3...Verification and Validation of SimulationSimulation Models; 4.3.1 Overview; 4.3.2 Verification and Validation of ABMs |
4.3.3 Related Work on V&V of ABMABM4.4...Overview of Communication Network Simulators; 4.4.1 Simulation of WSNs; 4.4.2 Simulation of P2P Networks; 4.4.3 Simulation of Robotic Swarms; 4.4.4 ABMABM for Complex Communication Networks SimulationSimulation; 4.5...Complex Network Modeling; 4.5.1 Complex Network Methods; 4.5.2 Theoretical Basis; 4.5.3 Centralities and Other Quantitative Measures; 4.5.3.1 Clustering Coefficient; 4.5.3.2 Matching Index; 4.5.4 Centrality Measures; 4.5.4.1 Degree Centrality; 4.5.4.2 Eccentricity Centrality; 4.5.4.3 Closeness Centrality |
4.5.4.4 Shortest Path Betweenness Centrality4.5.5 Software Tools for Complex NetworksComplex Networks; 4.6...Conclusions; References; Index |
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Sommario/riassunto |
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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 book 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 and epidemiological HIV. |
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