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IJCAI
2001
13 years 11 months ago
Complexity of Probabilistic Planning under Average Rewards
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Jussi Rintanen
MOR
2008
87views more  MOR 2008»
13 years 10 months ago
On Near Optimality of the Set of Finite-State Controllers for Average Cost POMDP
We consider the average cost problem for partially observable Markov decision processes (POMDP) with finite state, observation, and control spaces. We prove that there exists an -...
Huizhen Yu, Dimitri P. Bertsekas
SOCO
2010
Springer
13 years 4 months ago
Using evolution strategies to solve DEC-POMDP problems
Decentralized partially observable Markov decision process (DEC-POMDP) is an approach to model multi-robot decision making problems under uncertainty. Since it is NEXP-complete the...
Baris Eker, H. Levent Akin
DATE
2007
IEEE
92views Hardware» more  DATE 2007»
14 years 4 months ago
Dynamic power management under uncertain information
This paper tackles the problem of dynamic power management (DPM) in nanoscale CMOS design technologies that are typically affected by increasing levels of process, voltage, and te...
Hwisung Jung, Massoud Pedram
AMAI
2004
Springer
14 years 3 months ago
A Framework for Sequential Planning in Multi-Agent Settings
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Piotr J. Gmytrasiewicz, Prashant Doshi