Principles of artificial intelligence
Principles of artificial intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Polynomial-time algorithms for permutation groups
SFCS '80 Proceedings of the 21st Annual Symposium on Foundations of Computer Science
PPCP: efficient probabilistic planning with clear preferences in partially-known environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence
Finding patterns in an unknown graph
AI Communications - The Symposium on Combinatorial Search
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We study a subclass of POMDPs, called Deterministic POMDPs, that is characterized by deterministic actions and observations. These models do not provide the same generality of POMDPs yet they capture a number of interesting and challenging problems, and permit more efficient algorithms. Indeed, some of the recent work in planning is built around such assumptions mainly by the quest of amenable models more expressive than the classical deterministic models. We provide results about the fundamental properties of Deterministic POMDPs, their relation with AND/OR search problems and algorithms, and their computational complexity.