Operations Research
Management Science
Moment methods for decision analysis
Management Science
Decision Analysis by Augmented Probability Simulation
Management Science
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Decision Analysis
Solving linear-quadratic conditional Gaussian influence diagrams
International Journal of Approximate Reasoning
A forward-backward Monte Carlo method for solving influence diagrams
International Journal of Approximate Reasoning
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
From influence diagrams to junction trees
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
Influence diagrams (IDs) are powerful tools for representing and solving complex decision making problems. This paper presents a simulation-based approach for solving decision making problems formulated by hybrid IDs, which involve both discrete and continuous decision and chance variables. In the proposed method, Monte-Carlo simulation is applied in both approximating the expected conditional utility and solving the optimal decision strategies. The forward Monte-Carlo method is presented for expectation calculation, and it does not require Bayesian inference as in the standard "roll-back" method. The cross-entropy method in optimization is introduced to solve the optimal strategies. The decision variables are treated as random variables, and the decision strategies are solved by recursively updating the probability density of the decision variables. Finally, we present the simulation results of a bidding problem as an illustration.