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simulation of an agent-based model. This leads to the unsatisfactory
situation that the result of a simulation is often less dependend on
the model itself than on the currently used simulation engine.
In order to overcome these problems, the
General Reference Model
for Agent-based Modeling and Simulation
(GRAMS) is proposed. The
GRAMS reference model provides a domain-independent framework
for agent-based modeling and simulation.
The GRAMS reference model focuses primarily on the development
of conceptual models and may also guide development of formal models.
It provides precise definitions of key concepts of agent-based models
and specifies interrelations and dependencies of model components,
and finally defines the simulation of an agent-based model. In this
respect, the GRAMS reference model consolidates existing definitions
and puts them into an integrated framework. As a conceptual reference
model the GRAMS reference model is defined in a way that imposes
as few as possible constraints on the development of actual problem-
specific models.
Besides simplifying actual development processes by using the
GRAMS reference model as a template, understandability, traceabil-
ity, verification and validation, and reuse of models may be improved
due to increased comparability of models. The GRAMS reference
model defines basic concepts of agent-based modeling and simulation
and thus provides a low-level modeling approach. More sophisticated
concepts like specific agent architectures and protocols for multi-agent
interactions may be based on the GRAMS reference model.
For evaluation purposes, a representation of three basic agent ar-
chitectures within the GRAMS reference model is outlined (reactive
agents, deliberative agents and reflective agents). Furthermore, three
case studies have been analyzed to evaluate the practical applicability
of the GRAMS reference model. Regarding these case studies, the
GRAMS reference model can be applied effectively for conceptual
model development and does not impose any unnecessary or limiting
constraints. On the contrary, the evaluation results indicate that the
GRAMS reference model provides a valuable guideline throughout a
model development process.
