Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Automatically generating abstractions for planning
Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Path planning under time-dependent uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Transactions on Intelligent Transportation Systems
Hi-index | 0.00 |
We examine methods for a special class of path-planning problems in which the routes are constrained. The scenario could happen, for instance, in transit systems where passengers cannot order drivers to change the routes of public buses to meet individual travel needs. General search algorithms are applicable to this class of problems, but may not find the desired solution as efficiently as possible. This paper reports three different strategies that capture the route constraints for improving efficiency of path planning algorithms. The first strategy applies hierarchical planning, and the rest employs matrices for encoding route constraints. We propose and prove that the Q matrix is instrumental for capturing route constraints and measuring quality of service of the transportation network. Moreover, we discuss how we may apply the Q matrix in designing admissible heuristic functions that are crucial for applying the A* algorithm for best-path planning under route constraints.