Is it possible to predict on any level what terror organizations will do next? Looking at current events would seem to indicate that would be a tall undertaking but researchers are looking to newly developed software and a online portal to help change that.

The University of Maryland’s Institute for Advanced Computer Studies (UMIACS) announced today it is  developing software that can learn rules and forecast potential terrorist behavior.  

The group has rolled out the SOMA Terror Organization Portal (STOP), that will let experts query automatically learned rules on terrorist organization behavior, forecast potential behavior based on these rules and network with other analysts examining the same subjects.

The system uses Stochastic Opponent Modeling Agents, or SOMA, a formal, logical-statistical reasoning framework that uses data about past behavior of terror groups in order to learn rules about the probability of an organization, community, or person taking certain actions in different situations, researchers said in a release. SOMA has generated tens of thousands of rules about the likely behavior of each of about 30 groups, including major terrorist organizations such as Hezbollah, Hamas, and Hezb-I-Islami, researchers said.

“SOMA is a significant joint computer science and social science achievement that will facilitate learning about and forecasting terrorist group behavior based on rigorous mathematical and computational models,” said V.S. Subrahmanian, computer science professor and UMIACS director who heads the STOP project. “But even the best science needs to work hand in hand with social scientists and users. In addition to accurate behavioral models and forecasting algorithms, the SOMA Terror Organization Portal acts as a virtual roundtable that terrorism experts can gather around and form a rich community that transcends artificial boundaries.”

The SOMA Terror Organization Portal is funded by the Air Force Office of Scientific Research and has users from four defense agencies. The users, in addition to performing queries and running a prediction engine, can mark rules as useful or not useful and leave comments about the rules. They can learn what others have found useful and identify interesting rules and comments to.

“It takes a network to fight a network. Analysts need to learn from other analysts. This system allows multiple users to arrive at a shared understanding of how a terror group operates and what it might do in the future. Using the queries analysts can examine the underlying data and then, using the forecasting capabilities, test their theories,” said Aaron Mannes, a UMIACS researcher.   

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