Opportunities for multiagent systems and multiagent reinforcement learning in traffic control |
| |
Authors: | Ana L C Bazzan |
| |
Affiliation: | (1) Instituto de Informática, Universidade Federal do Rio Grande do Sul, CP 15064, 91501-970 Porto Alegre, RS, Brazil |
| |
Abstract: | The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general,
and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional
capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to
multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation
system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also
the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the “agentification” of
a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly
adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and
act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent
systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches
and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and
challenges so that future research in multiagent systems can address them. |
| |
Keywords: | Multiagent systems Multiagent learning Reinforcement learning Coordination of agents Game-theory Traffic signal control |
本文献已被 SpringerLink 等数据库收录! |
|