Divide and conquer under global constraints: a solution to the N-queens problem
Journal of Parallel and Distributed Computing
Neural network parallel computing
Neural network parallel computing
A dynamic programming solution to the n-queens problem
Information Processing Letters
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Automatically generating abstractions for planning
Artificial Intelligence
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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
Test-case generator for nonlinear continuous parameter optimizationtechniques
IEEE Transactions on Evolutionary Computation
Cooperative updating in the Hopfield model
IEEE Transactions on Neural Networks
An agent-based adaptive task-scheduling model for peer-to-peer computational grids
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
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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.