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GLOBECOM
2006
IEEE
15 years 9 months ago
Power Optimal Opportunistic Scheduling
Abstract— In this paper, we propose a power optimal opportunistic scheduling scheme for a multiuser single hop Time Division Multiple Access (TDMA) system. We formulate the probl...
Abhijeet Bhorkar, Abhay Karandikar, Vivek S. Borka...
AAAI
2010
15 years 4 months ago
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
EMO
2005
Springer
68views Optimization» more  EMO 2005»
15 years 8 months ago
Multi-objective Optimization of Problems with Epistemic Uncertainty
Abstract. Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-w...
Philipp Limbourg
WSC
2008
15 years 5 months ago
Approximate dynamic programming: Lessons from the field
Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applie...
Warren B. Powell
161
Voted
ILP
2004
Springer
15 years 8 months ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik