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ICML
2003
IEEE
16 years 8 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars
198
Voted
ICML
2002
IEEE
16 years 8 months ago
Hierarchically Optimal Average Reward Reinforcement Learning
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
Mohammad Ghavamzadeh, Sridhar Mahadevan
182
Voted
CHI
2009
ACM
16 years 8 months ago
Territorial coordination and workspace awareness in remote tabletop collaboration
There is growing interest in tabletop interfaces that enable remote collaboration by providing shared workspaces. This approach assumes that these remote tabletops afford the same...
Philip Tuddenham, Peter Robinson
CHI
2009
ACM
16 years 8 months ago
Back-of-device interaction allows creating very small touch devices
In this paper, we explore how to add pointing input capabilities to very small screen devices. On first sight, touchscreens seem to allow for particular compactness, because they ...
Patrick Baudisch, Gerry Chu
204
Voted
KDD
2008
ACM
110views Data Mining» more  KDD 2008»
16 years 8 months ago
Mining preferences from superior and inferior examples
Mining user preferences plays a critical role in many important applications such as customer relationship management (CRM), product and service recommendation, and marketing camp...
Bin Jiang, Jian Pei, Xuemin Lin, David W. Cheung, ...