Understanding the Behaviour of Learning-Based BDI Agents in the Braess' Paradox.

The Braess' paradox is a well-known problem associated with route choice and traffic distribution. Agent-based simulations that investigate this paradox typically model driver's behaviour using reactive agent architectures, which simplify and abstract an inherently complex behaviour. The BDI architecture is an alternative widely used in multiagent systems, which has not been evaluated as a suitable solution to deal with this problem. We thus in this paper detail an empirical evaluation of the BDI architecture, enhanced with a learning-based plan selection, to address the Braess' paradox. We describe the results of two simulations configured to reproduce the paradox behaviour. Results indicate that agents are able to soften the effects of the Braess' paradox using only local information, as opposed to existing alternatives, including when the environment is dynamic.