The complexity of Markov decision processes
Mathematics of Operations Research
A model for reasoning about persistence and causation
Computational Intelligence
Planning and control
Learning to Perceive and Act by Trial and Error
Machine Learning
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Planning under uncertainty: structural assumptions and computational leverage
New directions in AI planning
Fundamenta Informaticae - Special issue: intelligent information systems
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dynamic Programming
An integrated approach to dynamic decision-making under uncertainty
An integrated approach to dynamic decision-making under uncertainty
Representing and Solving Decision Problems with Limited Information
Management Science
Nonapproximability results for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Solving POMDPs by searching the space of finite policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Objective: The development of dynamic limited-memory influence diagrams as a framework for representing factorized infinite-horizon partially observable Markov decision processes (POMDPs), the introduction of algorithms for their (approximate) solution, and the application to a dynamic decision problem in clinical oncology. Materials and methods: A dynamic limited-memory influence diagram for high-grade carcinoid tumor pathophysiology was developed in collaboration with an expert physician. Three algorithms, known as single policy updating, single rule updating, and simulated annealing have been examined for approximating the optimal treatment strategy from a space of 10^1^9 possible strategies. Results: Single policy updating proved intractable for finding a treatment strategy for carcinoid tumors. Single rule updating and simulated annealing both found the treatment strategy that is applied by physicians in practice. Conclusions: Dynamic limited-memory influence diagrams are a suitable framework for the representation of factorized infinite-horizon POMDPs, and the developed algorithms find acceptable solutions under the assumption of limited memory about past observations. The framework allows for finding reasonable treatment strategies for complex dynamic decision problems in medicine.