Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
The use of models—making MABS more informative
MABS 2000 Proceedings of the second international workshop on Multi-agent based simulation
MASON: A Multiagent Simulation Environment
Simulation
Simulation for the Social Scientist
Simulation for the Social Scientist
When and how to develop domain-specific languages
ACM Computing Surveys (CSUR)
Experiences creating three implementations of the repast agent modeling toolkit
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Model-driven Development of Complex Software: A Research Roadmap
FOSE '07 2007 Future of Software Engineering
Eclipse development using the graphical editing framework and the eclipse modeling framework
Eclipse development using the graphical editing framework and the eclipse modeling framework
GAMA: An Environment for Implementing and Running Spatially Explicit Multi-agent Simulations
Agent Computing and Multi-Agent Systems
Requirements elicitation and analysis of multiagent systems using activity theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A framework for the definition of metamodels for Computer-Aided Software Engineering tools
Information and Software Technology
Multi-agent based simulation: where are the agents?
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Agent-based simulation replication: a model driven architecture approach
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Computational & Mathematical Organization Theory
Epistemological perspectives on simulation: overview and introduction
Computational & Mathematical Organization Theory
| Hi-index | 0.00 |
A major challenge in agent-based modelling is the management of the process to generate executable simulations from the initial conceptual models. This process is complex and usually involves several roles, which may raise communication problems due to the diverse backgrounds and perspectives of participants and the use of non-explicit knowledge. This situation demands a clear separation and precise definition of the multiple aspects of the process, in order to facilitate their understanding, grasp their relationships and develop them. This paper addresses this goal with a fine-step refinement process for information based on the use of domain-specific languages. It considers analysis contexts that include a particular theoretical framework, domain, type of problem and target platform. For a given context, the process formally defines modelling languages conceptually close to the different aspects relevant to it. It also defines mappings between concepts in those languages. Researchers develop simulations by specifying models with the languages, and share and refine information by using mappings between these models. This infrastructure provides guidance throughout the process and makes the information involved explicit. A case study of continuous double auctions illustrates the approach.