Using parallel computing to improve the scalability of models with BDI agents

Abstract

These last years have seen the development of several extensions of modeling platforms to include BDI agents. These extensions have allowed modelers with little knowledge in programming and artificial intelligence to develop their own cognitive agents. However, especially in large-scale simulations, the problem of the computational time required by such complex agents is still an open issue. In order to address this difficulty, we propose a parallel version of the BDI architecture integrated into the GAMA platform. We show through several case studies that this new parallel architecture is much more efficient in terms of execution time, while remaining easy to use even by non-computer scientists.