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certain operations. These operations serve to two purposes. First, they provide
a way to specify behaviors of the environments themselves (e.g., environment
responses to the actions of agents). Second, they allow the succinct specification
of several possible scenarios for an environment (e.g., several possible ways of
stimulating agents). This latter possibility is one of the great advantages offered
by the use of a process algebra as a semantic basis (e.g., an algebraic expression
a
b defines the non-deterministic possibility of either a or b ), and renders our
approach particularly unique insofar as environments for MASs are concerned.
We may now formulate questions concerning the analysis of our MASs:
+
- Since the semantics of EMMAS is given as an LTS, it follows that now we
need criteria for selecting paths in it. With such paths, we shall be able to
perform concrete simulations.
- Concerning implementation, we believe that the π -calculus base can be par-
ticularly useful, since we could implement its few elements in order to have
our whole model on top of it. A similar approach is taken by Wang and Wysk
[12]. More generally, there are programming languages based on π -calculus,
such as the Join-Calculus [1] and Pict [8].
Finally, though EMMAS is designed to work with a particular agent model [10],
it actually imposes few restrictions on the agents, and its principles are general.
Hence, it could perhaps be adapted to work with other agent models.
Acknowledgements
The authors would like to thank Prof. Dr. Marie-Claude Gaudel (Laboratoire de
Recherche en Informatique, Université Paris-Sud 11) for her numerous comments
and suggestions during the preparation of this work.
This project benefited from the financial support of Coordenação de Aper-
feiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq).
References
1. Fournet, C., Gonthier, G.: The join calculus: A language for distributed mobile
programming. In: Barthe, G., Dybjer, P., Pinto, L., Saraiva, J. (eds.) APPSEM
2000. LNCS, vol. 2395, pp. 268-332. Springer, Heidelberg (2002)
2. Gilbert, N., Bankers, S.: Platforms and methods for agent-based modeling. Pro-
ceedings of the National Academy of Sciences of the United States 99(supplement
3) (2002)
3. Luke, S., Cio-Revilla, C., Panait, L., Sullivan, K.: MASON: A new multi-agent
simulation toolkit (2004), http://cs.gmu.edu/~eclab/projects/mason/
4. Milner, R.: Communicating and Mobile Systems: the Pi-Calculus. Cambridge Uni-
versity Press, Cambridge (1999)
5. Minar, N., Burkhart, R., Langton, C., Askenazi, M.: The Swarm simulation system:
A toolkit for building multi-agent simulations (1996), working Paper 96-06-042
 
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