Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Integrating OO road network database, cases and knowledge for route finding
Proceedings of the 2001 ACM symposium on Applied computing
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
An Admissible Heuristic Search Algorithm
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
A Heuristic for Domain Independent Planning and Its Use in an Enforced Hill-Climbing Algorithm
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Speeding up the Convergence of Real-Time Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Controlling the learning process of real-time heuristic search
Artificial Intelligence
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
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Partial pathfinding using map abstraction and refinement
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
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
Stratified case-based reasoning: reusing hierarchical problem solving episodes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning to act using real-time dynamic programming
Artificial Intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Memory-based heuristics for explicit state spaces
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Contraction hierarchies: faster and simpler hierarchical routing in road networks
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
Moving target search with intelligence
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Real-time heuristic search with depression avoidance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
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Real-time heuristic search algorithms satisfy a constant bound on the amount of planning per action, independent of problem size. As a result, they scale up well as problems become larger. This property would make them well suited for video games where Artificial Intelligence controlled agents must react quickly to user commands and to other agents. actions. On the downside, real-time search algorithms employ learning methods that frequently lead to poor solution quality and cause the agent to appear irrational by re-visiting the same problem states repeatedly. The situation changed recently with a new algorithm, D LRTA*, which attempted to eliminate learning by automatically selecting subgoals. D LRTA* is well poised for video games, except it has a complex and memory-demanding pre-computation phase during which it builds a database of subgoals. In this paper, we propose a simpler and more memory-efficient way of pre-computering subgoals thereby eliminating the main obstacle to applying state-of-the-art real-time search methods in video games. The new algorithm solves a number of randomly chosen problems off-line, compresses the solutions into a series of subgoals and stores them in a database. When presented with a novel problem on-line, it queries the database for the most similar previously solved case and uses its subgoals to solve the problem. In the domain of pathfinding on four large video game maps, the new algorithm delivers solutions eight times better while using 57 times less memory and requiring 14% less pre-computation time.