Decision making in autonomous systems is particularly challenging in unknown and
changing complex environments, where providing a complete a priori representation is not …

The relevance of decision making in autonomous systems is intrinsically related to the
system capacity to discriminate its perception-action states. This is particularly challenging in …

This chapter studies the use of agent technology in the domain of vehicle control. More
specifically, it illustrates how agents can address the problem of collaborative driving. First …

Future urban road traffic management is an example of a socially relevant problem that can
be modeled as a large-scale, open, distributed system, composed of many autonomous …

The increasing demand for mobility in our society poses various challenges to traffic
engineering, computer science in general, and artificial intelligence and multiagent systems …

The problem of advanced intersection control is being discovered as a promising application
field for multiagent technology. In this context, drivers interact autonomously with a …

One of the main challenges of multi-agent learning lies in establishing convergence of the
algorithms, as, in general, a collection of individual, self-serving agents is not guaranteed to …

Transportation and traffic are currently undergoing a rapid increase in terms of both scale
and complexity. At the same time, an increasing share of traffic participants are being …

Due to the complexity of the natural world, a programmer cannot foresee all possible
situations, a connected and autonomous vehicle (CAV) will face during its operation, and …

Autonomous systems such as Connected Autonomous Vehicles (CAVs), assistive robots are
set improve the way we live. Autonomous systems need to be equipped with capabilities to …