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A Spatio-Temporal Multiagent Simulation Framework for Reusing Agents in Different Kinds of Scenarios

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Multiagent System Technologies (MATES 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9433))

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Abstract

In this paper a spatio-temporal simulation framework for multiagent systems is introduced. Its fundamental idea consists in the possibility to develop agents that can be easily deployed in different kinds of scenarios without adapting the agents’ percepts, actions or communication model to a specific scenario. This can be useful to observe and evaluate agents in the context of various scenarios, e. g. to measure their generality and adaptivity against different kinds of problems. To demonstrate the framework, two different example scenarios are considered that are both simulated with the same simple agent implementation.

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Notes

  1. 1.

    Only a small selection will be mentioned here as a brief overview over recent and earlier related approaches.

  2. 2.

    Note that this definition slightly differs from [1] to outline the concept of perspectives more clearly.

  3. 3.

    The edge’s weight is restricted to \(\mathbb {N}\), since the current implementation of the framework is based on both discrete time and space units. For alternative implementations the weight could also be extended to \(\mathbb {R}\).

  4. 4.

    Some attributes are semantically restricted to \(\mathbb {N}\) by nature (e. g. attributes for limiting the number of visit events). Other attributes could also be extended to \(\mathbb {R}\) in alternative implementations of the framework.

  5. 5.

    The project can be downloaded from GitHub: https://github.com/dapel/Abstract Swarm.

  6. 6.

    Note that only the simulation semantics of the graphical modeling language are formalized here, and not the action selection or the communication mechanism of the agents, which will be explained separately in Sects. 3.2 and 3.4.

  7. 7.

    Note that the semantic formalism may appear complicated to the reader, in contrast to the claims made in the beginning about the framework being easy to use. But users usually use the graphical modeling interface and therefore don’t have to deal with the formalism, which serves as a foundation of the simulation algorithm here.

  8. 8.

    Note that the agent model only serves as a simple example and does not necessarily lead to good solutions in the general case. More complex behaviors (e. g. adaptive, learning, knowledge-based or BDI-like agents) could be also implemented using the agent programming interface.

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Acknowledgements

The author would like to thank Matthias Thimm for constant feedback on this paper. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013), REVEAL (Grant agree number 610928).

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Authors and Affiliations

  1. Information Engineering Group, Technische Universität Dortmund, Dortmund, Germany

    Daan Apeldoorn

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  1. Daan Apeldoorn
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Correspondence to Daan Apeldoorn .

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Editors and Affiliations

  1. Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

    Jörg P. Müller

  2. Erasmus University Rotterdam, Rotterdam, The Netherlands

    Wolf Ketter

  3. Bar Ilan University, Ramat Gan, Israel

    Gal Kaminka

  4. Brandenburg University of Technology, Cottbus, Germany

    Gerd Wagner

  5. Delft University of Technology, Delft, The Netherlands

    Nils Bulling

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Apeldoorn, D. (2015). A Spatio-Temporal Multiagent Simulation Framework for Reusing Agents in Different Kinds of Scenarios. In: Müller, J., Ketter, W., Kaminka, G., Wagner, G., Bulling, N. (eds) Multiagent System Technologies . MATES 2015. Lecture Notes in Computer Science(), vol 9433. Springer, Cham. https://doi.org/10.1007/978-3-319-27343-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-27343-3_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-27343-3

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