Control strategies for a stochastic planner
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Qualitative probabilities for default reasoning, belief revision, and causal modeling
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LAO: a heuristic search algorithm that finds solutions with loops
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Dynamic Programming and Optimal Control, Two Volume Set
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic Programming
Finding optimal solutions to Rubik's cube using pattern databases
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Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
ICML '05 Proceedings of the 22nd international conference on Machine learning
A knowledge-based framework for multimedia adaptation
Applied Intelligence
A Q-decomposition and bounded RTDP approach to resource allocation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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Adaptive multi-robot wide-area exploration and mapping
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R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a Stochastic Resource Allocation Problem
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Probabilistic planning with clear preferences on missing information
Artificial Intelligence
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
International Journal of Artificial Intelligence and Soft Computing
Focused real-time dynamic programming for MDPs: squeezing more out of a heuristic
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
PPCP: efficient probabilistic planning with clear preferences in partially-known environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Simultaneous heuristic search for conjunctive subgoals
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
mGPT: a probabilistic planner based on heuristic search
Journal of Artificial Intelligence Research
Topological value iteration algorithm for Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning with graded fluents and actions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Topological order planner for POMDPs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bayesian real-time dynamic programming
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Conformant plans and beyond: Principles and complexity
Artificial Intelligence
Amsaa: a multistep anticipatory algorithm for online stochastic combinatorial optimization
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Practical routing in a cyclic MobiSpace
IEEE/ACM Transactions on Networking (TON)
New prioritized value iteration for Markov decision processes
Artificial Intelligence Review
Topological value iteration algorithms
Journal of Artificial Intelligence Research
Automating the evaluation of planning systems
AI Communications
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Recent algorithms like RTDP and LAO* combine the strength of Heuristic Search (HS) and Dynamic Programming (DP) methods by exploiting knowledge of the initial state and an admissible heuristic function for producing optimal policies without evaluating the entire space. In this paper, we introduce and analyze three new HS/DP algorithms. A first general algorithm schema that is a simple loop in which 'inconsistent' reachable states (i.e., with residuals greater than a given c) are found and updated until no such states are found, and serves to make explicit the basic idea underlying HS/DP algorithms, leaving other commitments aside. A second algorithm, that builds on the first and adds a labeling mechanism for detecting solved states based on Tarjan's strongly-connected components procedure, which is very competitive with existing approaches. And a third algorithm, that approximates the latter by enforcing the consistency of the value function over the likely' reachable states only, and leads to great time and memory savings, with no much apparent loss in quality, when transitions have probabilities that differ greatly in value.