Intractable problems in control theory
SIAM Journal on Control and Optimization
The complexity of Markov decision processes
Mathematics of Operations Research
Computationally feasible bounds for partially observed Markov decision processes
Operations Research
A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
Journal of the ACM (JACM)
Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Limits to parallel computation: P-completeness theory
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Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
On the complexity of partially observed Markov decision processes
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
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
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
The Complexity of Optimal Small Policies
Mathematics of Operations Research
Learning Policies with External Memory
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
MFCS '97 Proceedings of the 22nd International Symposium on Mathematical Foundations of Computer Science
Policy Iteration for Factored MDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dynamic Programming
Efficient dynamic-programming updates in partially observable Markov decision processes
Efficient dynamic-programming updates in partially observable Markov decision processes
The complexity of planning with partially-observable Markov decision processes
The complexity of planning with partially-observable Markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Planning and control in stochastic domains with imperfect information
Planning and control in stochastic domains with imperfect information
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
My brain is full: when more memory helps
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Solving POMDPs by searching the space of finite policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A method for speeding up value iteration in partially observable Markov decision processes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
Nearly deterministic abstractions of Markov decision processes
Eighteenth national conference on Artificial intelligence
Greedy linear value-approximation for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Bayesian sparse sampling for on-line reward optimization
ICML '05 Proceedings of the 22nd international conference on Machine learning
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence
Selecting treatment strategies with dynamic limited-memory influence diagrams
Artificial Intelligence in Medicine
Possibilistic Influence Diagrams
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Q-learning with linear function approximation
COLT'07 Proceedings of the 20th annual conference on Learning theory
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
On the Computational Complexity of Stochastic Controller Optimization in POMDPs
ACM Transactions on Computation Theory (TOCT)
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We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for finding control policies are unlikely to or simply don't have guarantees of finding policies within a constant factor or a constant summand of optimal. Here "unlikely" means "unless some complexity classes collapse," where the collapses considered are P = NP, P = PSPACE, or P = EXP. Until or unless these collapses are shown to hold, any control-policy designer must choose between such performance guarantees and efficient computation.