Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Artificial Intelligence
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Multi-Hierarchical Representation of Large-Scale Space: Applications to Mobile Robots
Multi-Hierarchical Representation of Large-Scale Space: Applications to Mobile Robots
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
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Optimized algorithms for multi-agent routing
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Efficient triangulation-based pathfinding
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
How Do Place and Objects Combine? "What-Where" Memory for Human-Like Agents
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Learning from multiple heuristics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Graph abstraction in real-time heuristic search
Journal of Artificial Intelligence Research
Dynamic control in real-time heuristic search
Journal of Artificial Intelligence Research
Inferring complex agent motions from partial trajectory observations
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The M2M Pathfinding Algorithm Based on the Idea of Granular Computing
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Evaluating strategies for running from the cops
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
A coarse-to-fine approach for fast path finding for mobile robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An analysis of map-based abstraction and refinement
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Path planning for complex 3D multilevel environments
Proceedings of the 24th Spring Conference on Computer Graphics
Case-based subgoaling in real-time heuristic search for video game pathfinding
Journal of Artificial Intelligence Research
Escaping heuristic depressions in real-time heuristic search
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Hierarchical path-finding based on decision tree
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
Hierarchical visibility for guaranteed search in large-scale outdoor terrain
Autonomous Agents and Multi-Agent Systems
Bridging the gap between refinement and heuristics in abstraction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Classical search algorithms such as A* or IDA* are useful for computing optimal solutions in a single pass, which can then be executed. But in many domains agents either do not have the time to compute complete plans before acting, or should not spend the time to do so, due to the dynamic nature of the environment. Extensions to A* such as LRTA* address this problem by gradually learning an exact heuristic function, but the learning process is quite slow. In this paper we introduce Partial-Refinement A* (PRA*), which can fully interleave planning and acting through path abstraction and refinement. We demonstrate the etfectiveness of PRA* in the domain of real-time strategy (RTS) games. In maps taken from popular RTS games. we show that PRA* is not only able to cleanly interleave planning and execution. but it is also able to do so with only minimal losses of optimality.