— Highly underactuated and passively adaptive robotic hands have shown great promise for robust performance in unstructured settings. In order to fully realize this potential, ef...
Many tasks can be described by sequences of actions that normally exhibit some form of structure and that can be represented by a grammar. This paper introduces FOSeq, an algorithm...
This paper presents a statistical learning approach to predicting people's bidding behavior in negotiation. Our study consists of multiple 2-player negotiation scenarios wher...
Inductive programming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming ...
In this paper, we propose an Active Learning (AL) framework for the Multi-Task Adaptive Filtering (MTAF) problem. Specifically, we explore AL approaches to rapidly improve an MTAF...