Understanding the strategies of attacks is crucial for security applications such as computer and network forensics, intrusion response, and prevention of future attacks. This pap...
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of c...
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru M...
— We describe algorithms to build self-assembling robot systems composed of active modular robots and passive bars. The distributed algorithms are based on locally optimal matchi...