Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
The trailblazer search: a new method for searching and capturing moving targets
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Admissible Heuristic Search Algorithm
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Eighteenth national conference on Artificial intelligence
Controlling the learning process of real-time heuristic search
Artificial Intelligence
Performance bounds for planning in unknown terrain
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
A Comparison of Fast Search Methods for Real-Time Situated Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The trailblazer search with a hierarchical abstract map
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Theta*: any-angle path planning on grids
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Graph abstraction in real-time heuristic search
Journal of Artificial Intelligence Research
Dynamic control in real-time heuristic search
Journal of Artificial Intelligence Research
Real-time heuristic search with a priority queue
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental Phi*: incremental any-angle path planning on grids
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning to generalize and reuse skills using approximate partial policy homomorphisms
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Theta*: any-angle path planning on grids
Journal of Artificial Intelligence Research
Automatic state abstraction for pathfinding in real-time video games
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
International Journal of Robotics Research
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Situated agents which use learning real-time search are well poised to address challenges of real-time path-finding in robotic and computer game applications. They interleave a local lookahead search with movement execution, explore an initially unknown map, and converge to better paths over repeated experiences. In this paper, we first investigate how three known extensions of the most popular learning real-time search algorithm (LRTA*) influence its performance in a path-finding domain. Then, we combine automatic state abstraction with learning real-time search. Our scheme of dynamically building a state abstraction allows us to generalize updates to the heuristic function, thereby speeding up learning. The novel algorithm converges up to 80 times faster than LRTA* with only one fifth of the response time of A*.