Machine learning for Time Interval Petri Nets

Creating Petri Net domain models faces the same challenges that confront all knowledge-intensive Al performance systems: model specification, knowledge acquisition, and refinement. Thus, a fundamental question to investigate is the degree to which automation can be used. This paper formulates the learning task and presents the first machine learning method for Time Interval Petri Net (TIPN) domain models. In a preliminary evaluation within a damage control domain, the method learned a nearly perfect model of fire spread augmented with temporal and spatial data.