Aligning social welfare and agent preferences to alleviate traffic congestion

Multiagent coordination algorithms provide unique insights into the challenging problem of alleviating traffic congestion. What is particularly interesting in this class of problem is that no individual action (e.g., leave at a given time) is intrinsically "bad" but that combinations of actions among agents lead to undesirable outcomes. As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. In general, the traffic problem can be approached from two distinct perspectives: (i) from a city manager's point of view, where the aim is to optimize a city wide objective function (e.g., minimize total city wide delays), and (ii) from the individual driver's point of view, where each driver is aiming to optimize a personal objective function (e.g., a"timeliness"function that minimizes the difference desired and actual arrival times at a destination). In many c...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Kagan Tumer, Zachary T. Welch, Adrian K. Agogino
• Keywords: ATAL 2008, City Manager, Intelligent Agents, Manager's Objective Function, Objective Function