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HICSS
2003
IEEE
207views Biometrics» more  HICSS 2003»
14 years 20 days ago
Formalizing Multi-Agent POMDP's in the context of network routing
This paper uses partially observable Markov decision processes (POMDP’s) as a basic framework for MultiAgent planning. We distinguish three perspectives: first one is that of a...
Bharaneedharan Rathnasabapathy, Piotr J. Gmytrasie...
ATAL
2010
Springer
13 years 8 months ago
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...
W. Bradley Knox, Peter Stone
CORR
2008
Springer
173views Education» more  CORR 2008»
13 years 7 months ago
Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications
In this paper, we propose a general cross-layer optimization framework in which we explicitly consider both the heterogeneous and dynamically changing characteristics of delay-sens...
Fangwen Fu, Mihaela van der Schaar
BCB
2010
140views Bioinformatics» more  BCB 2010»
13 years 2 months ago
Guiding belief propagation using domain knowledge for protein-structure determination
A major bottleneck in high-throughput protein crystallography is producing protein-structure models from an electrondensity map. In previous work, we developed Acmi, a probabilist...
Ameet Soni, Craig A. Bingman, Jude W. Shavlik
IJRR
2011
218views more  IJRR 2011»
13 years 2 months ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...