Representation and Recognition of Uncertain Enemy Policies Using Statistical Models
Robert Suzić · Defense Technical Information Center (DTIC) · 2004
In this work we extend from the single agent to the on-line multi-agent stochastic policy recognition problem using a network structure. By using knowledge of agents interrelations we can create a policy structure that is compatible with that of a hostile military organisation. Using this approach we make use of existing knowledge about the military organisation and thereby strongly reduce the size of the hypothesis space. In this way we are able to bring down the problem complexity to a level that is tractable. Also, by using statistical models in policy recognition we are able to deal with uncertainty in a consistent way. This means that we have achieved improved policy recognition robustness. We have developed a proof of concept Bayesian Network model. For the information fusion purpose, we show with our model that it is possible to integrate the pre-processed uncertain dynamical sensor data such as the enemy position and combine this knowledge with terrain data and uncertain a priori knowledge such as the doctrine knowledge to infer multi-agent policy in a robust and statistically sound manner. 1.0