AI Technologies for Homeland Security
Papers from the AAAI Spring Symposium
John Yen and Robert Popp, Cochairs
After September 11, 2001, preempting terrorist acts,and providing for the security of citizens at home and abroad have become top priorities for the United States and many other nations around the globe. To achieve these goals, an overwhelming amount of information needs to be absorbed, processed, interpreted and analyzed in a timely fashion. Various AI technologies can be of great utility in addressing these challenges. For example, multiagent systems can support information sharing and collaboration among analysts, data mining techniques can discover and extract hidden patterns about terrorist activities buried in large data stores, social network analysis can help assess and predict terrorist intentions and behaviors, and knowledge representations and ontologies can facilitate information fusion, knowledge sharing and semantic understanding. However, using AI technologies to provide for the security of citizens and the homeland raises many complex issues, for example:
- Can AI technologies augment the ability of human analysts to objectively analyze large quantities of complex, oftentimes ambiguous or contradictory data while simultaneously reducing the impact of their personal biases?
- Can AI technologies be used to enhance collaboration between human and robots in service of homeland security?
- Can AI technologies facilitate information/knowledge sharing and semantic understanding while avoiding cognitive overload?
- Can AI technologies be used in information sharing and data mining applications to improve security while yet also enhancing the privacy of citizens?