A Formal Environment Model for Multi-Agent Systems

Silva, Paulo Salem da; Melo, Ana Duarte

doi:10.1007/978-3-642-19829-8_5

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…It would therefore be beneficial to move in the direction of [41] and represent the continuous parts of the system as an abstract automata, or a similar formulation, which allowed us to control the number of possible states. This would draw on work such as [64,73,80].…”

Section: Discussionmentioning

confidence: 99%

Practical verification of decision-making in agent-based autonomous systems

et al. 2014

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We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex logical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this development. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a separate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems.

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Section: Discussionmentioning

confidence: 99%

Practical verification of decision-making in agent-based autonomous systems

et al. 2014

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“…When this is the case, we can give a formal model for the environment and treat the agents therein as black-boxes. 33…”

Section: Automation Of Experimentsmentioning

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%

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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Behaviorist Agent Architecture

Silva¹,

Melo²

2013

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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A Formal Environment Model for Multi-Agent Systems

Cited by 5 publications

References 8 publications

Practical verification of decision-making in agent-based autonomous systems

Practical verification of decision-making in agent-based autonomous systems

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

Behaviorist Agent Architecture

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