Abstract:

The discriminative model update (DMU) is a common algorithm for solving interactive dynamic influence diagrams (I-DIDs). The lookahead method is used to give an improved discriminative model update algorithm which determines approximate behavior equivalence. Firstly, the models that are approximately behavior equivalent are clustered into a representative model set. Then the models within the representative model set are updated from top to bottom. In the updating process, only the models whose predictive behavior is different from others are updated. Compared with the DMU algorithm, the proposed algorithm can quickly and effectively reduce the model’s number, thus reducing the storage space and the running time of the computer, and improving the efficiency of the algorithm. The effectiveness of the proposed method is verified through experiments on the multiagent tiger and multiagent machine maintenance problems.