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AAAI
1996
15 years 3 months ago
Rewarding Behaviors
Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solu...
Fahiem Bacchus, Craig Boutilier, Adam J. Grove
ICARCV
2008
IEEE
170views Robotics» more  ICARCV 2008»
15 years 8 months ago
Mixed state estimation for a linear Gaussian Markov model
— We consider a discrete-time dynamical system with Boolean and continuous states, with the continuous state propagating linearly in the continuous and Boolean state variables, a...
Argyris Zymnis, Stephen P. Boyd, Dimitry M. Gorine...
ATAL
2004
Springer
15 years 7 months ago
Unifying Temporal and Structural Credit Assignment Problems
Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common dilemma known as the credit assignment problem. Multi-agent systems have the st...
Adrian K. Agogino, Kagan Tumer
ICTAI
2005
IEEE
15 years 7 months ago
Planning with POMDPs Using a Compact, Logic-Based Representation
Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...
Chenggang Wang, James G. Schmolze
ICC
2008
IEEE
169views Communications» more  ICC 2008»
15 years 8 months ago
Optimality of Myopic Sensing in Multi-Channel Opportunistic Access
—We consider opportunistic communications over multiple channels where the state (“good” or “bad”) of each channel evolves as independent and identically distributed Mark...
Tara Javidi, Bhaskar Krishnamachari, Qing Zhao, Mi...