Multi Agent Based Simulation Xix
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Multi-Agent-Based Simulation XIX
This book constitutes the thoroughly refereed post-conference proceedings of the 19th International Workshop on Multi-Agent-Based Simulation, MABS 2019, held in Stockholm Sweden, in July 2018 as part of the Federated AI Meeting, FAIM 2018. The 10 revised full papers included in this volume were carefully selected from 15 submissions. They focus on finding efficient solutions to model complex social systems in such areas as economics, management, and organisational and social sciences. In all these areas, agent theories, metaphors, models, analysis, experimental designs, empirical studies, and methodological principles, converge into simulation as a way of achieving explanations and predictions, exploration and testing of hypotheses, better designs and systems.
The Agent Modeling Language - AML
Author: Radovan Cervenka
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-04-17
Multi-agent systems have been a focus of studies for more than 25 years. Yet, despite substantial effort of an active research community, modeling of multi-agent systems still lacks complete and proper definition, general acceptance, and practical application. This book provides the Agent-Modeling Language (AML), a comprehensive modeling language as an extension of UML 2.0, concentrating on multi-agent systems and applications.
Multi-Agent-Based Simulation XXII
This book constitutes the thoroughly refereed post-conference proceedings of the 21st International Workshop on Multi-Agent-Based Simulation, MABS 2021, held in May 2021 as part of AAMAS 2021. The conference was held virtually due to COVID 19 pandemic. The 14 revised full papers included in this volume were carefully selected from 23 submissions. The workshop focused on finding efficient solutions to model complex social systems, in such areas as economics, management, organizational and social sciences in general. In all these areas, agent theories, metaphors, models, analysis, experimental designs, empirical studies, and methodological principles, all converge into simulation as a way of achieving explanations and predictions, exploration and testing of hypotheses, better designs and systems and providing decision-support in a wide range of applications.