Authors:
Fabien Badeig
1
;
Flavien Balbo
1
and
Suzanne Pinson
2
Affiliations:
1
Université Paris-Dauphine; INRETS Institute, Gretia Laboratory, France
;
2
Université Paris-Dauphine, France
Keyword(s):
Multi-agent-based simulation framework, Scheduling policy, Environment, Contextual activation.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Simulation
;
Software Engineering
;
Symbolic Systems
Abstract:
Multi-agent-based simulation (MABS) is used to understand complex real life processes and to experiment several scenarios in order to reproduce, understand and evaluate these processes. A crucial point in the design of a multi-agent-based simulation is the choice of a scheduling policy. In classical multi-agent-based simulation frameworks, a pitfall is the fact that the action phase, based on local agent context analysis, is repeated in each agent at each time cycle during the simulation execution. This analysis inside the agents reduces agent flexibility and genericity and limits agent behavior reuse in various simulations. If the designer wants to modify the way the agent reacts to the context, he could not do it without altering the way the agent is implemented because the link between agent context and agent actions is an internal part of the agent. In contrast to classical approaches, our proposition, called EASS (Environment as Active Support for Simulation), is a new multi-age
nt-based simulation framework, where the context is analyzed by the environment and where agent activation is based on context evaluation. This activation process is what we call contextual activation. The main advantage of contextual activation is the improvement of complex agent simulation design in terms of flexibility, genericity and agent behavior reuse.
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