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  • Policy Learning of Static Bayesian Networks

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This menu item is only available in Validation mode for static networks having Decision nodes and Utility nodes. It makes possible to execute an algorithm based on dynamic programming to find the optimal action policy. This policy describes the actions to apply in order to obtain the maximum expected utility. The global utility is defined by the sum of all the utilities of the network. The resulting policy can be directly read in the quality table of each Decision node. The quality corresponds to the expected utility if one applies the action and then applies the actions specified by BayesiaLab. The action to apply, the one with the greatest quality, appears in light blue in the quality table and in the monitors.