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ROBOCUP
2004
Springer
114views Robotics» more  ROBOCUP 2004»
16 years 5 days ago
Modular Learning System and Scheduling for Behavior Acquisition in Multi-agent Environment
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since othe...
Yasutake Takahashi, Kazuhiro Edazawa, Minoru Asada
ISCAS
2003
IEEE
117views Hardware» more  ISCAS 2003»
16 years 3 days ago
Learning temporal correlations in biologically-inspired aVLSI
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
Adria Bofill-i-Petit, Alan F. Murray
ECTEL
2006
Springer
15 years 10 months ago
Who Needs "Blended Learning"? Some Thoughts on a Political Concept
The paper covers the topic from an e-learning provider's perspective on the basis of practical experience and discussions with corporate and SME partners. In this paper the au...
Ray Mary Rosdale
ICALT
2009
IEEE
15 years 10 months ago
On-demand Construction of Personalized Learning Experiences Using Semantic Web and Web 2.0 Techniques
Nowadays, the Semantic Web technologies are exploited also in the e-learning domain in order to provide personalized and adaptive learning experiences, semantic annotation of lear...
Nicola Capuano, Matteo Gaeta, Francesco Orciuoli, ...
AAAI
2008
15 years 9 months ago
Instance-level Semisupervised Multiple Instance Learning
Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-...
Yangqing Jia, Changshui Zhang