Abstract:

The model space of interactive dynamic influence diagrams (I-DIDs) is too large and the number of candidate models grows exponentially with the number of time steps. To deal with the high calculation cost issue， a method of solving I-DIDs approximately that combines approximate behavioral principle and discriminative model update algorithm (DMU) is proposed. First, a new definition of behavior equivalence and approximate behavior equivalence of models are presented. Then the candidate models based on the Kullback-Leibler (KL) distance and the action of candidate models are clustered. Afterwards, the top to bottom method is used to merge policy trees into policy graphs. Finally, I-DIDs are solved by using the approach of DMU. The simulation results show that the approximated algorithm can dramatically decrease the number of candidate model and improve the efficiency compared with the traditional DMU algorithm. This research work should be valuable in the research and application of I-DIDs.