LEADER 05521nam 2200661 a 450 001 9910437823103321 005 20200520144314.0 010 $a1-283-74213-6 010 $a94-007-3852-8 024 7 $a10.1007/978-94-007-3852-2 035 $a(CKB)2670000000280522 035 $a(EBL)971837 035 $a(OCoLC)817917018 035 $a(SSID)ssj0000790471 035 $a(PQKBManifestationID)11428975 035 $a(PQKBTitleCode)TC0000790471 035 $a(PQKBWorkID)10745752 035 $a(PQKB)10798384 035 $a(DE-He213)978-94-007-3852-2 035 $a(MiAaPQ)EBC971837 035 $z(PPN)258864923 035 $a(PPN)168336421 035 $a(EXLCZ)992670000000280522 100 $a20120831d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCognitive agent-based computing-I $ea unified framework for modeling complex adaptive systems using agent-based & complex network-based methods /$fMuaz A. Niazi, Amir Hussain 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (65 p.) 225 0 $aSpringerBriefs in cognitive computation,$x2212-6023 300 $aDescription based upon print version of record. 311 $a94-007-3851-X 320 $aIncludes bibliographical references and index. 327 $aCognitive 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 327 $aReferences3 Complex Adaptive Systems; 3.1...Overview; 3.2...Complex 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 327 $a4.1.2 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 327 $a4.3.3 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 327 $a4.5.4.4 Shortest Path Betweenness Centrality4.5.5 Software Tools for Complex NetworksComplex Networks; 4.6...Conclusions; References; Index 330 $aComplex Systems are made up of numerous interacting sub-components. Non-linear interactions of these components or agents give rise to emergent behavior observable at the global scale. Agent-based modeling and simulation is a proven paradigm which has previously been used for effective computational modeling of complex systems in various domains. Because of its popular use across different scientific domains, research in agent-based modeling has primarily been vertical in nature. The goal of this book is to provide a single hands-on guide to developing cognitive agent-based models for the exploration of emergence across various types of complex systems. We present practical ideas and examples for researchers and practitioners for the building of agent-based models using a horizontal approach - applications are demonstrated in a number of exciting domains as diverse as wireless sensors networks, peer-to-peer networks, complex social systems, research networks and epidemiological HIV. 410 0$aSpringerBriefs in Cognitive Computation,$x2212-6023 606 $aSocial systems$xMathematical models 606 $aSocial sciences$xMathematical models 615 0$aSocial systems$xMathematical models. 615 0$aSocial sciences$xMathematical models. 676 $a006.3 700 $aNiazi$b Muaz A$01058427 701 $aHussain$b A$g(Amir)$01750483 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437823103321 996 $aCognitive agent-based computing-I$94185128 997 $aUNINA