A multi-agent based system for route planning

Abstract

Route-planning is a perplexing problem in the field of computer science. Finding computationally viable solutions to route-planning is a challenging task. Various techniques have been devised to solve this problem; however, there is a trade-off in these techniques between computational complexity and accuracy. One paradigm defined in artificial intelligence which can contribute to this problem’s solution is a multiagent system, in which a group of agents work together to achieve a common goal. These agents require a robust mechanism for coordination and information exchange in order to achieve this goal. When applied to route-planning, these agents interact to compute the optimal path based on distance, speed, and a variety of environmental factors that may impact travelling time. The consideration of these factors allows a solution generated by the system to be applicable to real world sitations. In this work, a hybrid model is proposed which utilizes a multi-agent system to compute travelling time for a given route. This system utilizes a Bayesian network to model interdependencies which exist between environmental factors. In order to illustrate the potential effectiveness of the proposed model, several case studies from the state of Alabama are presented.