This paper proposes an approach to modelling and performance pre-diction of large multi-agent systems, based on the Theatre actor system. The approach rests on Uppaal for formal modelling, graphical reasoning and prelim-inary property checking, and on Java for enabling large model sizes and execu-tion benefits on a multi-core machine. As a significant case study, the Minority Game (MG) binary game often used in economics, natural and social sciences, is chosen for modelling and analysis. In MG a population of agents/players compete, without explicit interactions, in the use of a shared and scarce re-source. At each step, each player has to decide if to use or not the resource, by understanding that when the majority of agents decides to exploit the resource, an inevitable congestion would arise. In classic MG, although each player learns from the experience, it is unable to improve its behavior/performance. A genetic variant of MG is then considered which by using cross-over and muta-tion on local strategies allows a bad-performing player to possibly improve its attitude. The paper shows an MG formal actor model, which is then trans-formed into Java for parallel execution. Experimental results confirm good exe-cution speedup when the size of the model is scaled to large values, as required by practical applications.

Performance prediction of scalable multi-agent systems using Parallel Theatre

Nigro Libero;Cicirelli Franco
2021

Abstract

This paper proposes an approach to modelling and performance pre-diction of large multi-agent systems, based on the Theatre actor system. The approach rests on Uppaal for formal modelling, graphical reasoning and prelim-inary property checking, and on Java for enabling large model sizes and execu-tion benefits on a multi-core machine. As a significant case study, the Minority Game (MG) binary game often used in economics, natural and social sciences, is chosen for modelling and analysis. In MG a population of agents/players compete, without explicit interactions, in the use of a shared and scarce re-source. At each step, each player has to decide if to use or not the resource, by understanding that when the majority of agents decides to exploit the resource, an inevitable congestion would arise. In classic MG, although each player learns from the experience, it is unable to improve its behavior/performance. A genetic variant of MG is then considered which by using cross-over and muta-tion on local strategies allows a bad-performing player to possibly improve its attitude. The paper shows an MG formal actor model, which is then trans-formed into Java for parallel execution. Experimental results confirm good exe-cution speedup when the size of the model is scaled to large values, as required by practical applications.
Actors, Theatre framework, Minority Game, genetic algorithm, evolutionary learning, performance prediction, Uppaal, multi-core machines, Java.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/323493
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