Integrating free-flow architectures with role models based on statecharts

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Abstract

Engineering non-trivial open multi-agent systems is a challenging task. Our research focusses on situated multi-agent systems, i.e. systems in which agents are explicitly placed in a context -an environment- which agents can perceive and in which they can act. Two concerns are essential in developing such open systems. First, the agents must be adaptive in order to exhibit suitable behavior in changing circumstances of the system: new agents may join the system, others may leave, the environment may change, e.g. its topology or its characteristics such as throughput and visibility. A well-known family of agent architectures for adaptive behavior are free-flow architectures. However, building a free-flow architecture based on an analysis of the problem domain is a quasi-impossible job for non-trivial agents. Second, multi-agent systems developers as software engineers require suitable abstractions for describing and structuring agent behavior. The abstraction of a role obviously is essential in this respect. Earlier, we proposed Statecharts as a formalism to describe roles. Although this allows application developers to describe roles comfortably, the formalism supports rigid behavior only, and hampers adaptive behavior in changing environments. In this paper we describe how a synergy can be reached between free-flow architectures and Statechart models in order to combine the best of both worlds: adaptivity and suitable abstractions. We illustrate the result through a case study on controlling a collection of automated guided vehicles (AGVs), which is the subject of an industrial project. © Springer-Verlag Berin Heidelberg 2005.

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APA

Weyns, D., Steegmans, E., & Holvoet, T. (2005). Integrating free-flow architectures with role models based on statecharts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3390 LNCS, pp. 104–120). Springer Verlag. https://doi.org/10.1007/978-3-540-31846-0_7

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