Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Principles of artificial intelligence
Principles of artificial intelligence
Admissibility of AO* when heuristics overestimate
Artificial Intelligence
Heuristic search in restricted memory (research note)
Artificial Intelligence
Artificial Intelligence
Control strategies for a stochastic planner
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Optimizing decision trees through heuristically guided search
Communications of the ACM
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Planning and Control in Artificial Intelligence: A Unifying Perspective
Applied Intelligence
A Planning Graph Heuristic for Forward-Chaining Adversarial Planning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Restricted value iteration: theory and algorithms
Journal of Artificial Intelligence Research
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fault-tolerant planning under uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Heuristic search algorithms can find solutions that take the form of a simple path (A*), a tree or an acyclic graph (AO*). We present a novel generalization of heuristic search (called LAO*) that can find solutions with loops, that is, solutions that take the form of a cyclic graph. We show that it can be used to solve Markov decision problems without evaluating the entire state space, giving it an advantage over dynamic-programming algorithms such as policy iteration and value iteration as an approach to stochastic planning.