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Automatic Generation of Self-monitoring MASs from Multiparty Global Session Types in Jason

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7784)

Abstract

Global session types are behavioral types designed for specifying in a compact way multiparty interactions between distributed components, and verifying their correctness. We take advantage of the fact that global session types can be naturally represented as cyclic Prolog terms - which are directly supported by the Jason implementation of AgentSpeak - to allow simple automatic generation of self-monitoring MASs: given a global session type specifying an interaction protocol, and the implementation of a MAS where agents are expected to be compliant with it, we define a procedure for automatically deriving a self-monitoring MAS. Such a generated MAS ensures that agents conform to the protocol at run-time, by adding a monitor agent that checks that the ongoing conversation is correct w.r.t. the global session type.

The feasibility of the approach has been experimented in Jason for a non-trivial example involving recursive global session types with alternative choice and fork type constructors. Although the main aim of this work is the development of a unit testing framework for MASs, the proposed approach can be also extended to implement a framework supporting self-recovering MASs.

Keywords

  • Multiagent System
  • Automatic Generation
  • Belief Base
  • Transition Step
  • Interaction Protocol

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Ancona, D., Drossopoulou, S., Mascardi, V. (2013). Automatic Generation of Self-monitoring MASs from Multiparty Global Session Types in Jason. In: Baldoni, M., Dennis, L., Mascardi, V., Vasconcelos, W. (eds) Declarative Agent Languages and Technologies X. DALT 2012. Lecture Notes in Computer Science(), vol 7784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37890-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-37890-4_5

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