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ECSQARU
2007
Springer
14 years 1 months ago
Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions
Abstract. The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as con...
Emmanuel Ramasso, Michèle Rombaut, Denis Pe...
ICASSP
2011
IEEE
12 years 11 months ago
Stochastic resource allocation for cognitive radio networks based on imperfect state information
Efficient design of cognitive radio networks calls for secondary users implementing adaptive resource allocation, which requires knowledge of the channel state information in ord...
Antonio G. Marqués, Georgios B. Giannakis, ...
SAC
2005
ACM
14 years 27 days ago
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process
This paper presents a novel model of reinforcement learning agents. A feature of our learning agent model is to integrate analytic hierarchy process (AHP) into a standard reinforc...
Kengo Katayama, Takahiro Koshiishi, Hiroyuki Narih...
INFOCOM
2009
IEEE
14 years 2 months ago
Scheduling in Mobile Ad Hoc Networks with Topology and Channel-State Uncertainty
—We study throughput-optimal scheduling/routing over mobile ad-hoc networks with time-varying (fading) channels. Traditional back-pressure algorithms (based on the work by Tassiu...
Lei Ying, Sanjay Shakkottai
AAAI
1996
13 years 8 months ago
Efficient Goal-Directed Exploration
If a state space is not completely known in advance, then search algorithms have to explore it sufficiently to locate a goal state and a path leading to it, performing therefore w...
Yury V. Smirnov, Sven Koenig, Manuela M. Veloso, R...