Operations Research
Valuation-based systems for Bayesian decision analysis
Operations Research
Representing and Solving Asymmetric Bayesian Decision Problems
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Representing and Solving Decision Problems with Limited Information
Management Science
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
A comparison of two approaches for solving unconstrained influence diagrams
International Journal of Approximate Reasoning
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
Using Conditional Random Fields for Decision-Theoretic Planning
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Sequential influence diagrams: A unified asymmetry framework
International Journal of Approximate Reasoning
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Cost-sensitive classification with unconstrained influence diagrams
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
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We extend the language of influence diagrams to cope with decision scenarios where the order of decisions and observations is not determined. As the ordering of decisions is dependent on the evidence, a step-strategy of such a scenario is a sequence of dependent choices of the next action. A strategy is a step-strategy together with selection functions for decision actions. The structure of a step-strategy can be represented as a DAG with nodes labeled with action variables. We introduce the concept of GS-DAG: a DAG incurporating an optimal step-strategy for any instantiation. We give a method for constructing GS-DAGs, and we show how to use a GS-DAG for determining an optimal strategy. Finally we discuss how analysis of relevant past can be used to reduce the size of the GS-DAG.