Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
IEEE Transactions on Knowledge and Data Engineering
Incremental heuristic search in AI
AI Magazine
Speeding up moving-target search
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Learning in real-time search: a unifying framework
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
Fast replanning for navigation in unknown terrain
IEEE Transactions on Robotics
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Generalized Fringe-Retrieving A*: faster moving target search on state lattices
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Algorithms for continuous location-dependent and context-aware queries in indoor environments
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A new approach for continual planning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Context-aware modelling of continuous location-dependent queries in indoor environments
Journal of Ambient Intelligence and Smart Environments - Context Awareness
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Incremental search algorithms reuse information from previous searches to speed up the current search and are thus often able to find shortest paths for series of similar search problems faster than by solving each search problem independently from scratch. However, they do poorly on moving target search problems, where both the start and goal cells change over time. In this paper, we thus develop Fringe-Retrieving A* (FRA*), an incremental version of A* that repeatedly finds shortest paths for moving target search in known gridworlds. We demonstrate experimentally that it runs up to one order of magnitude faster than a variety of state-of-the-art incremental search algorithms applied to moving target search in known gridworlds.