Detection and simulation of scenarios with hidden Markov models and event dependency graphs

The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios-i.e., time sequences of structured data. For simulation, we construct two models-hidden Markov models (HMMs) and event dependency graphs. Combined, these two simulation methods allow the specification of dependencies in event ordering, simultaneous execution of multiple scenarios, and evolving networks of data. For scenario recognition, we consider the application of multi-grained HMMs. We explore, in detail, mismatch between training scenarios and simulated test scenarios. The methods are applied to terrorist scenario detection with a simulation coded by a subject matter expert.