Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Real-time search for learning autonomous agents
Real-time search for learning autonomous agents
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Real-time search in non-deterministic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning to act using real-time dynamic programming
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
Easy and hard testbeds for real-time search algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Terrain coverage with ant robots: a simulation study
Proceedings of the fifth international conference on Autonomous agents
Efficient and inefficient ant coverage methods
Annals of Mathematics and Artificial Intelligence
Towards Building Terrain-Covering Ant Robots
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Building Terrain-Covering Ant Robots: A Feasibility Study
Autonomous Robots
Cooperative Cleaners: A Study in Ant Robotics
International Journal of Robotics Research
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
Multi-robot exploration and terrain coverage in an unknown environment
Robotics and Autonomous Systems
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Real-time search methods have successfully been used to solve a large variety of search problems but their properties are largely unknown. In this paper, we study how existing real-time search methods scale up. We compare two realtime search methods that have been used successfully in the literature and differ only in the update rules of their values: Node Counting, a real-time search method that always moves to the successor state that it has visited the least number of times so far, and Learning Real-Time A*, a similar real-time search method. Both real-time search methods seemed to perform equally well in many standard domains from artificial intelligence. Our formal analysis is therefore surprising. We show that the performance of Node Counting can be exponential in the number of states even in undirected domains. This solves an open problem and shows that the two real-time search methods do not always perform similarly in undirected domains since the performance of Learning RealTime A* is known to be polynomial in the number of states at worst.