Implementation and integration of a Bayesian Network for prediction of tactical intention into a ground target simulator

Prediction of the enemy's intention is a main issue of threat analysis, and, hence, will be an important part of the C2-systems of tomorrow. A technique that can be useful for this kind of predictions is Bayesian networks (BNs). We have developed a BN for prediction of the enemy's tactical intention, and the implemented BN has been integrated into a ground target simulation framework. The general problem of how to find appropriate prior distributions for BNs has been addressed by developing a tool for data collection, which may make it easier to come up with appropriate prior distributions, by learning conditional probability tables from collected cases, i.e. parameter learning

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