1 option
Multi-Agent-Based Simulation XXV : 25th International Workshop, MABS 2024, Auckland, New Zealand, May 6, 2024, Revised Selected Papers / edited by Jason Thompson, Ivana Stankov.
Springer Nature - Springer Computer Science (R0) eBooks 2025 English International Available online
View online- Format:
- Book
- Series:
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 15583
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Social sciences--Data processing.
- Social sciences.
- Application software.
- Education--Data processing.
- Education.
- Computer networks.
- Artificial Intelligence.
- Computer Application in Social and Behavioral Sciences.
- Computer and Information Systems Applications.
- Computers and Education.
- Computer Communication Networks.
- Local Subjects:
- Artificial Intelligence.
- Computer Application in Social and Behavioral Sciences.
- Computer and Information Systems Applications.
- Computers and Education.
- Computer Communication Networks.
- Physical Description:
- 1 online resource (IX, 99 p. 29 illus., 24 illus. in color.)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-88017-X
- OCLC:
- 1523375274
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.