Fujitsu Develops AI Technology to Quickly Solve Urban Security Positioning Problems

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Fujitsu Laboratories Ltd. and the University of Electro-Communications today announced the development of a high-speed algorithm that uses mathematical game theory as an artificial intelligence technology to aid in the development of security planning. This will work to solve city-scale road network security problems, such as where best to position checkpoints when trying to catch a criminal.

For security measures at locations where people gather, it is often not possible to completely seal off all intrusion or escape routes with limited security resources, so it is necessary to effectively deploy security personnel and to minimize anticipated damage. The formulation of security plans has relied on the experience of experts and intuition, but in recent years there has been a focus on game theory, which mathematically describes both offence and defense, as a technology to support expert decision-making. However, it has been difficult to apply game theory to a city-scale security problem of catching criminals at checkpoints in real-world cities because the processing volume expands exponentially with the scale of the road network.

Now, using Fujitsu Laboratories' proprietary network contraction technology, Fujitsu Laboratories and the University of Electro-Communications have developed an algorithm to rapidly solve city-scale road network security problems. Compared with previous technology, this makes it possible to find the theoretically optimal security plan 20 times faster, on average, for a 100-node problem, and 500 times faster, on average, for a 200-node problem. For 200,000-node problems, on the scale of Tokyo's 23 wards, where formulating a plan would have taken several days with previous technology, this technology can generate a security plan in approximately five minutes, enabling interactive planning support.

Fujitsu Laboratories aims to commercialize this technology as part of Fujitsu Limited's AI technology, Human Centric AI Zinrai ("Zinrai"), during fiscal 2017. The University of Electro-Communications plans to proceed with the expansion of this technology beyond city-scale road networks.

Details of this technology will be announced at the International Conference on Autonomous Agents and Multiagent Systems 2016 (AAMAS 2016), one of the world's largest conferences in the AI and multiagent field, to be held in Singapore on May 9th.

Development Background

Security problems at places where people gather, such as cities and airports, could ideally be solved if all paths used by criminals could be sealed off, but because this is difficult to achieve with limited security resources in the vast majority of cases, there is a need for effective deployment of limited security resources according to the movement and psychological characteristics of criminals. The formulation of security plans has historically relied on the experience of experts and intuition, but in recent years, when there has been a demand for advanced security to face new threats, such as organized crime, the use of AI to formulate security plans has been attracting attention. In particular, technology using game theory, which treats both the criminal's side and the security side as opposing decision makers, called security games, is beginning to come into practical use as a tool to help experts make decisions.

Issues

The city-scale road network security problem is a security game problem with the goal of catching criminals at such locations as checkpoints when they are trying to either reach their target or escape. However, the number of movement patterns for the criminal (paths from the intrusion point to the target of their attack) grows exponentially in response to the scale of the road network (the number of roads). This has meant that it was previously impossible to solve problems with large numbers of nodes (intersections), representing the road network of a city, in a realistic amount of processing time, making application to real-world scenarios difficult.

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