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NIPS
2004
15 years 5 months ago
Approximately Efficient Online Mechanism Design
Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make valu...
David C. Parkes, Satinder P. Singh, Dimah Yanovsky
NIPS
2003
15 years 5 months ago
Ambiguous Model Learning Made Unambiguous with 1/f Priors
What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
Gurinder S. Atwal, William Bialek
NIPS
2003
15 years 5 months ago
An MDP-Based Approach to Online Mechanism Design
Online mechanism design (MD) considers the problem of providing incentives to implement desired system-wide outcomes in systems with self-interested agents that arrive and depart ...
David C. Parkes, Satinder P. Singh
ICML
2010
IEEE
15 years 5 months ago
Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...
Tobias Lang, Marc Toussaint
IJAR
2008
119views more  IJAR 2008»
15 years 4 months ago
Adapting Bayes network structures to non-stationary domains
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process stru...
Søren Holbech Nielsen, Thomas D. Nielsen