A modular technique for the design of efficient distributed leader finding algorithms
ACM Transactions on Programming Languages and Systems (TOPLAS)
Artificial Intelligence
Proceedings of the seventh international conference (1990) on Machine learning
Navigating in unfamiliar geometric terrain
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
How to learn an unknown environment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Incremental path planning on graphs with cycles
Proceedings of the first international conference on Artificial intelligence planning systems
Learning continuous-space navigation heuristics in real time
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Piecemeal Learning of an Unknown Environment
Machine Learning - Special issue on COLT '93
Graph learning with a nearest neighbor approach
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Graph learning with a nearest neighbor approach
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
OBDD-based universal planning for synchronized agents in non-deterministic domains
Journal of Artificial Intelligence Research
Utility-based on-line exploration for repeated navigation in an embedded graph
Artificial Intelligence
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If a state space is not completely known in advance, then search algorithms have to explore it sufficiently to locate a goal state and a path leading to it, performing therefore what we call goal-directed exploration. Two paradigms of this process are pure exploration and heuristic-driven exploitation: the former approaches explore the state space using only knowledge of the physically visited portion of the domain, whereas the latter approaches totally rely on heuristic knowledge to guide the search towards goal states. Both approaches have disadvantages: the first one does not utilize available knowledge to cut down the search effort, and the second one relies too much on the knowledge, even if it is misleading. We have therefore developed a framework for goal-directed exploration, called VECA, that combines the advantages of both approaches by automatically switching from exploitation to exploration on parts of the state space where exploitation does not perform well. VECA provides better performance guarantees than previously studied heuristic-driven exploitation algorithms, and experimental evidence suggests that this guarantee does not deteriorate its average-case performance.