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NIPS
1993
15 years 7 months ago
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
Tommi Jaakkola, Michael I. Jordan, Satinder P. Sin...
AIPS
2009
15 years 7 months ago
Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing
Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a se...
Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla
ATAL
2010
Springer
15 years 7 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
GECCO
2008
Springer
145views Optimization» more  GECCO 2008»
15 years 7 months ago
Memory with memory: soft assignment in genetic programming
Based in part on observations about the incremental nature of most state changes in biological systems, we introduce the idea of Memory with Memory in Genetic Programming (GP), wh...
Nicholas Freitag McPhee, Riccardo Poli
GECCO
2008
Springer
175views Optimization» more  GECCO 2008»
15 years 7 months ago
Using differential evolution for symbolic regression and numerical constant creation
One problem that has plagued Genetic Programming (GP) and its derivatives is numerical constant creation. Given a mathematical formula expressed as a tree structure, the leaf node...
Brian M. Cerny, Peter C. Nelson, Chi Zhou