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» Guiding Inference Through Relational Reinforcement Learning
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NIPS
2008
13 years 9 months ago
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
Actor-critic algorithms for reinforcement learning are achieving renewed popularity due to their good convergence properties in situations where other approaches often fail (e.g.,...
Dotan Di Castro, Dmitry Volkinshtein, Ron Meir
ICDM
2007
IEEE
162views Data Mining» more  ICDM 2007»
13 years 11 months ago
Exploiting Network Structure for Active Inference in Collective Classification
Active inference seeks to maximize classification performance while minimizing the amount of data that must be labeled ex ante. This task is particularly relevant in the context o...
Matthew J. Rattigan, Marc Maier, David Jensen, Bin...
NIPS
2001
13 years 9 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr
GAMEON
2007
13 years 9 months ago
Agent Based Virtual Tutorship and E-Learning Techniques Applied to a Business Game Built on System Dynamics
An advanced Business Game is presented in the paper, built on the methodology of System Dynamics. It can be used for cognitive learning and knowledge transmission in schools and U...
Marco Remondino
VL
2010
IEEE
209views Visual Languages» more  VL 2010»
13 years 6 months ago
Automatically Inferring ClassSheet Models from Spreadsheets
Many errors in spreadsheet formulas can be avoided if spreadsheets are built automatically from higher-level models that can encode and enforce consistency constraints. However, d...
Jacome Cunha, Martin Erwig, Joao Saraiva