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@InProceedings{AAAI18-Sharon,
  author = {Guni Sharon and Michael Albert and Tarun Rambha and Stephen Boyles and Peter Stone},
  title = {Traffic Optimization For a Mixture of Self-interested and Compliant Agents},
  booktitle = {Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18)},
  location = {New Orleans, Lousiana, USA},
  month = {February},
  year = {2018},
    abstract = {
     This paper focuses on two commonly used path assignment policies for
       agents traversing a congested network: \textit{self-interested routing},
       and \textit{system-optimum routing}. In the self-interested routing policy
       each agent selects a path that optimizes its own utility, while in the
       system-optimum routing, agents are assigned paths with the goal of
       maximizing system performance. This paper considers a scenario where a
       centralized network manager wishes to optimize utilities over all
       agents, i.e., implement a system-optimum routing policy. In many
       real-life scenarios, however, the system manager is unable to influence
       the route assignment of all agents due to limited influence on route
       choice decisions. Motivated by such scenarios, a computationally
       tractable method is presented that computes the minimal amount of agents
       that the system manager needs to influence (compliant agents) in order
       to achieve system optimal performance. Moreover, this methodology can
       also determine whether a given set of compliant agents is sufficient to
       achieve system optimum and compute the optimal route assignment for the
       compliant agents to do so. Experimental results are presented showing
       that in several large-scale, realistic traffic networks optimal flow can
       be achieved with as low as 13\% of the agent being compliant and up to
       54\%.},
}