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ILP
2003
Springer
15 years 8 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
ITS
2010
Springer
168views Multimedia» more  ITS 2010»
15 years 8 months ago
Computational Workflows for Assessing Student Learning
The use of technology for instruction, and the enormous amount of information available for consumption, places a considerable burden on instructors who must learn to integrate app...
Jun Ma, Erin Shaw, Jihie Kim
SIGECOM
1999
ACM
136views ECommerce» more  SIGECOM 1999»
15 years 7 months ago
Automated strategy searches in an electronic goods market: learning and complex price schedules
Markets for electronic goods provide the possibility of exploring new and more complex pricing schemes, due to the flexibility of information goods and negligible marginal cost. I...
Christopher H. Brooks, Scott A. Fay, Rajarshi Das,...
EELC
2006
113views Languages» more  EELC 2006»
15 years 7 months ago
A Hybrid Model for Learning Word-Meaning Mappings
Abstract. In this paper we introduce a model for the simulation of language evolution, which is incorporated in the New Ties project. The New Ties project aims at evolving a cultur...
Federico Divina, Paul Vogt
ATAL
2008
Springer
15 years 5 months ago
Non-linear dynamics in multiagent reinforcement learning algorithms
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agent...
Sherief Abdallah, Victor R. Lesser