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Multi-Agent-Based Simulation XXV: 25th International Workshop, MABS 2024, Auckland, New Zealand, May 6, 2024, Revised Selected Papers [Mīkstie vāki]

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  • Formāts: Paperback / softback, 99 pages, height x width: 235x155 mm, 24 Illustrations, color; 5 Illustrations, black and white; IX, 99 p. 29 illus., 24 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15583
  • Izdošanas datums: 06-Apr-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031880161
  • ISBN-13: 9783031880162
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Multi-Agent-Based Simulation XXV: 25th International Workshop, MABS 2024, Auckland, New Zealand, May 6, 2024, Revised Selected PapersPalielināt
  • Formāts: Paperback / softback, 99 pages, height x width: 235x155 mm, 24 Illustrations, color; 5 Illustrations, black and white; IX, 99 p. 29 illus., 24 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 15583
  • Izdošanas datums: 06-Apr-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031880161
  • ISBN-13: 9783031880162
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031880162.html
Keywords:

This book constitutes the refereed proceedings of the 25th International Workshop on Multi-Agent-Based Simulation XXV, MABS 2024, held in Auckland, New Zealand, on May 6, 2024.

The 7 full papers included in this book were carefully reviewed and selected from 11 submissions. They are organized in topical sections as follows: MABS methodology and tools; MABS education; and MABS applications.

.- MABS Methodology and Tools.


.- Creating a Serious Game on top of an Agent-Based Simulation, an applied
case to crisis management and population evacuation.


.- GENSIMO A Generic Framework for Modelling Social Insurance Systems.


.- Are Low Emission Zones Effective in Reducing Emissions and Ambient Air
Pollution?.


.- MABS Education.


.- Teaching Agent-based Modeling for Simulating Social Systems A
Research-based Learning Approach.


.- MABS Applications.


.- KEMASS: Knowledge-Enhanced Multi-Agent simulation for energy Scheduling
Support.


.- Inverse Generative Approach for Identifying Agent-Based Models from
Stochastic Primitives.


.- Inferring pedestrian decision-making through inverse reinforcement
learning.