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HIS
2007
13 years 9 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
AIPS
2004
13 years 9 months ago
Optimal Resource Allocation and Policy Formulation in Loosely-Coupled Markov Decision Processes
The problem of optimal policy formulation for teams of resource-limited agents in stochastic environments is composed of two strongly-coupled subproblems: a resource allocation pr...
Dmitri A. Dolgov, Edmund H. Durfee
WSC
2001
13 years 9 months ago
Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization
Tactical and operational planning for manufacturing enterprises are more important today than ever before as their supply chains span the globe. Two state-of-the-art technologies ...
Peter Lendermann, Boon-Ping Gan, Leon F. McGinnis
AIPS
2000
13 years 9 months ago
On-line Scheduling via Sampling
1 We consider the problem of scheduling an unknown sequence of tasks for a single server as the tasks arrive with the goal off maximizing the total weighted value of the tasks serv...
Hyeong Soo Chang, Robert Givan, Edwin K. P. Chong
NIPS
1998
13 years 9 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch