Verification of behaviourist multi-agent systems by means of formally guided simulations

Silva, Paulo Salem da

doi:10.11606/t.45.2011.tde-10042012-100539

Cited by 3 publications

(3 citation statements)

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“…Thanks to the operational semantics of the π -calculus, it is possible to translate (on-the-fly) our high-level representation into an ordinary ATS and explore it using a simulation interface as described in Section 8.1. For more details, see da Silva 11 and da Silva and de Melo. 33…”

Section: Discussionmentioning

confidence: 99%

“…However, more complex and detailed examples can be found in the work of da Silva. 11 The examples shown there explicitly use agent and environment models to generate M , whose formalisms are out of the scope of the present work, but can be found partly in da Silva and de Melo 33,34 .…”

Section: Another Examplementioning

confidence: 99%

“…In actual applications, this may be significant. In the case of the MASs we studied in da Silva, 11 for example, the maximum number m of successors of states in M is O ( n 2 ) , where n is the size of the MAS environment under consideration. As in this case, other applications must refine the analysis of complexities to account for the inner working of the simulation model.…”

Section: A1 Justification Of Completenessmentioning

confidence: 99%

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On-the-fly verification of discrete event simulations by means of simulation purposes: Extended version

Silva

Melo

2013

SIMULATION

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Discrete event simulations can be used to analyze natural and artificial phenomena. To this end, one provides models whose behaviors are characterized by discrete events in a discrete timeline. By running such a simulation, one can then observe its properties. This suggests the possibility of applying on-the-fly verification procedures during simulations. In this work we propose a method by which this can be accomplished. It consists of modeling the simulation as a transition system (implicitly), and the property to be verified as another transition system (explicitly). The latter we call a simulation purpose and it is used both to verify the success of the property and to guide the simulation. Algorithmically, this corresponds to building a synchronous product of these two transitions systems on-the-fly and using it to operate a simulator. By the end of such an algorithm, it may deliver either a conclusive or inconclusive verdict. If conclusive, it becomes known whether the simulation model satisfies the simulation purpose. If inconclusive, it is possible to adjust certain parameters and try again. The precise nature of simulation purposes, as well as the corresponding satisfiability relations and verification algorithms, are largely determined by methodological considerations important for the analysis of simulations, whose computational characteristics we compare with empirical scientific procedures. We provide a number of ways in which such a satisfiability relation can be defined formally, the related algorithms, and mathematical proofs of soundness, completeness and complexities. Two application examples are given to illustrate the approach.

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