Principles of artificial intelligence
Principles of artificial intelligence
The complexity of Markov decision processes
Mathematics of Operations Research
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
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
BI-POMDP: Bounded, Incremental, Partially-Observable Markov-Model Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
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
An online POMDP algorithm for complex multiagent environments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Tractable planning under uncertainty: exploiting structure
Tractable planning under uncertainty: exploiting structure
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Stochastic local search for POMDP controllers
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Approximate planning for factored POMDPs using belief state simplification
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A frame-based probabilistic framework for spoken dialog management using dialog examples
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
A Markov Model for Multiagent Patrolling in Continuous Time
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A stochastic point-based algorithm for POMDPs
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Efficient planning under uncertainty with macro-actions
Journal of Artificial Intelligence Research
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Run-time improvement of point-based POMDP policies
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Point-based online value iteration algorithm in large POMDP
Applied Intelligence
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Solving large Partially Observable Markov Decision Processes (POMDPs) is a complex task which is often intractable. A lot of effort has been made to develop approximate offline algorithms to solve ever larger POMDPs. However, even state-of-the-art approaches fail to solve large POMDPs in reasonable time. Recent developments in online POMDP search suggest that combining offline computations with online computations is often more efficient and can also considerably reduce the error made by approximate policies computed offline. In the same vein, we propose a new anytime online search algorithm which seeks to minimize, as efficiently as possible, the error made by an approximate value function computed offline. In addition, we show how previous online computations can be reused in following time steps in order to prevent redundant computations. Our preliminary results indicate that our approach is able to tackle large state space and observation space efficiently and under real-time constraints.