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|z 0123970415
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|a (OCoLC)900242339
|z (OCoLC)898771617
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|a TE228.3
|b .A38 2015
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|a BUS
|x 070100
|2 bisacsh
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|x 009000
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|a 388.3/12
|2 23
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|a Advances in artificial transportation systems and simulation /
|c edited by Rosaldo J.F. Rosetti, Ronghui Liu.
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264 |
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1 |
|a San Diego, California :
|b Elsevier/Academic Press,
|c [2015]
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300 |
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|a 1 online resource (xvi, 241 pages .)
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336 |
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|a text
|b txt
|2 rdacontent
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337 |
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|a computer
|b c
|2 rdamedia
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338 |
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|a online resource
|b cr
|2 rdacarrier
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490 |
1 |
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|a Intelligent systems series
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504 |
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|a Includes bibliographical references and index.
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588 |
0 |
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|a Print version record.
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|a The Intelligent Systems Series encompasses theoretical studies, design methods, and real-world implementations and applications. It publishes titles in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Titles focus on professional and academic reference works and handbooks. This volume, Advances in Artificial Transportation Systems and Simulation, covers hot topics including driver assistance systems; cooperative vehicle-highway systems; collision avoidance; pedestrian protection; image, radar and lidar signal processing; and V2V and V2I communications. The readership for the series is broad, reflecting the wide range of intelligent systems interest and application, but focuses on engineering (in particular automation, control, mechatronics, robotics, transportation, automotive, aerospace), electronics and electronic design, and computer science.
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0 |
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|a Cover; Title Page; Copyright Page; Table of Contents; List of contributors; Preface; Chapter 1 -- ITSUMO: An Agent-Based Simulator for Intelligent Transportation Systems; 1.1 -- Introduction and Motivation; 1.2 -- Description of the Simulator; 1.2.1 -- Microscopic Simulation Model and Simulation Kernel; 1.2.2 -- Database Module; 1.2.3 -- Output Module: Statistics and Visualization; 1.3 -- Control: Traffic Light Agent Module; 1.3.1 -- Greedy Traffic Light Agent; 1.3.2 -- Reinforcement Learning-Based Methods; 1.3.3 -- Swarm-Intelligence Inspired Signal Plan Choice; 1.4 -- Demand.
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|a 1.4.1 -- Routing of the Demand1.4.2 -- Deadlock Handling; 1.4.3 -- Driver Definition; 1.4.4 -- Drivers and En-Route Replanning; 1.5 -- Case-Study: Aggregating Intelligence to Traffic Simulation; 1.6 -- Conclusion; Acknowledgment; References; Chapter 2 -- A Pattern-Based Framework for Building Self-Organizing Multi-Agent Systems; 2.1 -- Introduction; 2.2 -- JASOF; 2.2.1 -- Main Idea; 2.2.2 -- JASOF Structure; 2.2.2.1 -- Environment; 2.2.2.2 -- Agent location; 2.2.2.3 -- Diffusion pattern; 2.2.2.4 -- Evaporation pattern; 2.2.2.5 -- Aggregation pattern; 2.2.2.6 -- Replication pattern; 2.2.3 -- JASOF Hotspots.
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|a 2.3 -- Case Study: A Self-Organized Automatic Guided Vehicle2.3.1 -- Main Idea; 2.3.2 -- Destination Agent; 2.3.3 -- Warehouse Agent; 2.3.4 -- Transporter Agent; 2.3.5 -- Location Agent; 2.3.6 -- Execution; 2.3.7 -- System Composition; 2.4 -- Related Work; 2.5 -- Conclusions and Future Work; Acknowledgment; References; Chapter 3 -- An Agent Methodology for Processes, the Environment, and Services; 3.1 -- Introduction; 3.2 -- Background; 3.3 -- Analysis and Design for MAS; 3.3.1 -- Scenario Description and Early Requirements Analysis; 3.3.2 -- Analysis Phase; 3.3.3 -- Architectural Design.
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|a 3.3.4 -- Detailed Design3.4 -- Discussion and Future Work; Acknowledgment; References; Chapter 4 -- A Role-Based Method for Analyzing Supply Chain Models; 4.1 -- Introduction; 4.2 -- Related Work; 4.3 -- A Framework of Supply Chain Roles, Responsibilities and Interactions; 4.3.1 Roles; 4.3.2 -- Responsibilities; 4.3.3 -- Interaction; 4.3.4 -- An Illustrating Example; 4.4 -- A Method for Analyzing Supply Chain Simulation Models; 4.5 -- Applicability and Validity of the Framework and Analysis Method; 4.5.1 -- Analysis of the TAPAS Simulation Model.
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|a 4.5.2 -- Analysis of a Supply Chain Model by Strader et al. (1998)4.5.3 -- Analysis of a Supply Chain Model by Gjerdrum et al. (2001); 4.5.4 -- Discrete Event Simulation of a Food Supply Chain; 4.5.5 -- Dynamic Simulation of a Short Life Cycle Product Supply Chain; 4.6 -- Concluding Remarks and Future Work; References; Chapter 5 -- Applying Delegate MAS Patterns in Designing Solutions for Dynamic Pickup and Delivery Problems; 5.1 -- Introduction; 5.2 -- Related Work; 5.2.1 -- Combinatorial Optimization-Based Approaches for Solving PDP; 5.2.2 -- MAS-Based Approaches for Solving PDP.
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|a 5.2.3 -- Patterns for MAS.
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650 |
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0 |
|a Intelligent transportation systems.
|
650 |
|
0 |
|a Multiagent systems.
|
650 |
|
2 |
|a Transportation
|0 (DNLM)D014186
|
650 |
|
6 |
|a Syst�emes de transport intelligents.
|0 (CaQQLa)201-0252717
|
650 |
|
6 |
|a Syst�emes multiagents (Intelligence artificielle)
|0 (CaQQLa)000268008
|
650 |
|
7 |
|a BUSINESS & ECONOMICS
|x Industries
|x Transportation.
|2 bisacsh
|
650 |
|
7 |
|a TRANSPORTATION
|x Public Transportation.
|2 bisacsh
|
650 |
|
7 |
|a Intelligent transportation systems
|2 fast
|0 (OCoLC)fst01723430
|
650 |
|
7 |
|a Multiagent systems
|2 fast
|0 (OCoLC)fst01749717
|
700 |
1 |
|
|a Rossetti, Rosaldo J. F.,
|e editor.
|
700 |
1 |
|
|a Liu, Ronghui,
|e editor.
|
776 |
0 |
8 |
|i Print version:
|z 9780123970411
|z 0123970415
|
830 |
|
0 |
|a Intelligent systems series.
|
856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780123970411
|z Texto completo
|