Operations Research
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Structuring conditional relationships in influence diagrams
Operations Research
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
The Art of Causal Conjecture
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Conditional independence and chain event graphs
Artificial Intelligence
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Sequential influence diagrams: A unified asymmetry framework
International Journal of Approximate Reasoning
From influence diagrams to junction trees
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
From the Editors---Games and Decisions in Reliability and Risk
Decision Analysis
From Reliability Block Diagrams to Fault Tree Circuits
Decision Analysis
| Hi-index | 0.00 |
Decision analysis problems have traditionally been solved using either decision trees or influence diagrams. Although decision trees are better at handling asymmetry, prevalent in many reliability and risk analysis problems, influence diagrams can solve larger real-world problems by exploiting conditional independence. Decision circuits are graphical representations that combine the computational benefits of both graphical models. They are syntactic representations, i.e., they depict the summation, multiplication, and maximization operations required to solve a decision analysis problem. Previous work on decision circuits has focused on compiling them automatically from influence diagrams and describing the ways in which they can be used for efficient solution and sensitivity analysis. In this paper, we show how a decision circuit can be formulated directly, with or without the preprocessing of numbers that are assessed from the decision maker. By constructing two decision circuits for a nuclear reactor example, one using probabilities in inferred form and the other using probabilities in assessed form, we show how decision circuits generalize decision trees. The notion of coalescence is also made more explicit because computations for decision analysis can be saved and then reused in several ways. Because of their generality, decision circuits provide the analyst with a great deal of flexibility in problem formulation.