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JAIR
2007
127views more  JAIR 2007»
15 years 5 months ago
Learning Symbolic Models of Stochastic Domains
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
Hanna M. Pasula, Luke S. Zettlemoyer, Leslie Pack ...
ICPR
2008
IEEE
16 years 6 months ago
Learning weighted distances for relevance feedback in image retrieval
We present a new method for relevance feedback in image retrieval and a scheme to learn weighted distances which can be used in combination with different relevance feedback metho...
Enrique Vidal, Hermann Ney, Roberto Paredes, Thoma...
ATAL
2005
Springer
15 years 10 months ago
Coordinated exploration in multi-agent reinforcement learning: an application to load-balancing
This paper is concerned with how multi-agent reinforcement learning algorithms can practically be applied to real-life problems. Recently, a new coordinated multi-agent exploratio...
Katja Verbeeck, Ann Nowé, Karl Tuyls
IJCSA
2008
96views more  IJCSA 2008»
15 years 5 months ago
Integration of Educational Specifications and Standards to Support Adaptive Learning Scenarios in ADAPTAPlan
ADAPTAPlan project provides dynamic assistance for reducing authors' effort in developing instructional design tasks using user modelling, planning and machine learning techn...
Silvia Baldiris, Olga C. Santos, Carmen Barrera, J...
NECO
2010
136views more  NECO 2010»
15 years 3 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton