Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
— Embedded processors are required to achieve high performance while running on batteries. Thus, they must exploit all the possible means available to reduce energy consumption w...
We present a technique for synthesizing power- as well as area-optimized circuits from hierarchical data flow graphs under throughput constraints. We allow for the use of complex...
Cooperative problem solving with resource constraints are important in practical multi-agent systems. Resource constraints are necessary to handle practical problems including dis...
Toshihiro Matsui, Hiroshi Matsuo, Marius Silaghi, ...
Dynamic plan execution strategies allow an autonomous agent to respond to uncertainties while improving robustness and reducing the need for an overly conservative plan. Executive...
Patrick R. Conrad, Julie A. Shah, Brian C. William...