A machine learning technique for handling scenarios of interaction between conflicting agents is suggested. Scenarios are represented by directed graphs with labeled vertices (for ...
Boris Galitsky, Sergei O. Kuznetsov, Mikhail V. Sa...
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find stru...
The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While tempor...
— This paper addresses recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such meas...
Imran N. Junejo, Emilie Dexter, Ivan Laptev, Patri...