Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
A dynamization of the all pairs least cost path problem
Proceedings on STACS 85 2nd annual symposium on theoretical aspects of computer science
Admissibility of AO* when heuristics overestimate
Artificial Intelligence
Artificial Intelligence
On the dynamic shortest path problem
Journal of Information Processing
Incremental algorithms for minimal length paths
Journal of Algorithms
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
Agent searching in a tree and the optimality of iterative deepening
Artificial Intelligence
An incremental algorithm for a generalization of the shortest-path problem
Journal of Algorithms
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fully dynamic output bounded single source shortest path problem
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Divide-and-Conquer Frontier Search Applied to Optimal Sequence Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Multiple sequence alignment using anytime A*
Eighteenth national conference on Artificial intelligence
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
Anytime replanning using local subplan replacement
Anytime replanning using local subplan replacement
Sweep A*: Space-Efficient Heuristic Search in Partially Ordered Graphs
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Fully dynamic shortest paths in digraphs with arbitrary arc weights
Journal of Algorithms
Speeding up the convergence of online heuristic search and scaling up offline heuristic search
Speeding up the convergence of online heuristic search and scaling up offline heuristic search
Search-based planning for large dynamic environments
Search-based planning for large dynamic environments
Journal of Artificial Intelligence Research
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning by rewriting: efficiently generating high-quality plans
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Easy and hard testbeds for real-time search algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Weighted A∗ search -- unifying view and application
Artificial Intelligence
Time-bounded lattice for efficient planning in dynamic environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
HGA*, an efficient algorithm for path planning in a plane
Scientific and Technical Information Processing
ID* Lite: improved D* Lite algorithm
Proceedings of the 2011 ACM Symposium on Applied Computing
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance
Journal of Intelligent and Robotic Systems
BT*: an advanced algorithm for anytime classification
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Anytime algorithms for biobjective heuristic search
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
UNTANGLED: A Game Environment for Discovery of Creative Mapping Strategies
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Landmark-based heuristics and search control for automated planning (extended abstract)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A hierarchical approach for primitive-based motion planning and control of autonomous vehicles
Robotics and Autonomous Systems
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Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A^*-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs.