Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
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ACM Transactions on Programming Languages and Systems (TOPLAS)
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Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Incremental path planning on graphs with cycles
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Linear-space best-first search
Artificial Intelligence
Machine Learning - Special issue on reinforcement learning
Graph learning with a nearest neighbor approach
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Complexity Analysis of Real-Time Reinforcement Learning Applied to Finding Shortest Paths in Deterministic Domains
A Method for Solving Arbitrary-Wall Mazes by Computer
IEEE Transactions on Computers
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Are many reactive agents better than a few deliberative ones?
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'91 Proceedings of the 12th 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
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
Value-update rules for real-time search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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
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ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Anytime search in dynamic graphs
Artificial Intelligence
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
Derandomizing Random Walks in Undirected Graphs Using Locally Fair Exploration Strategies
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Coverage, exploration, and deployment by a mobile robot and communication network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Agent-centered search: situated search with small look-ahead
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Although researchers have studied which factors influence the behavior of traditional search algorithms, currently not much is known about how domain properties influence the performance of real-time search algorithms. In this paper we demonstrate, both theoretically and experimentally, that Eulerian state spaces (a superset of undirected state spaces) are very easy for some existing real-time search algorithms to solve: even real-time search algorithms that can be intractable, in general, are efficient for Eulerian state spaces. Because traditional real-time search testbeds (such as the eight puzzle and gridworlds) are Eulerian, they cannot be used to distinguish between efficient and inefficient real-time search algorithms. It follows that one has to use non-Eulerian domains to demonstrate the general superiority of a given algorithm. To this end, we present two classes of hard-to-search state spaces and demonstrate the performance of various real-time search algorithms on them.