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ILP
2007
Springer
16 years 1 months ago
Building Relational World Models for Reinforcement Learning
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
SOFSEM
2007
Springer
16 years 29 days ago
Incremental Learning of Planning Operators in Stochastic Domains
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
Javad Safaei, Gholamreza Ghassem-Sani
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
16 years 11 days ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
HICSS
2003
IEEE
209views Biometrics» more  HICSS 2003»
16 years 4 days ago
Divergent Approaches and Converging Views: Drawing Sensible Linkages between Knowledge Management and Organizational Learning
Knowledge management (KM) and organizational learning (OL) have developed in both divergent and convergent ways. In particular, these fields have relatively distinct intellectual ...
Jing Zhang, Sue R. Faerman
ICDM
2002
IEEE
105views Data Mining» more  ICDM 2002»
15 years 11 months ago
Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing
We empirically evaluate the performance of various reinforcement learning methods in applications to sequential targeted marketing. In particular, we propose and evaluate a progre...
Naoki Abe, Edwin P. D. Pednault, Haixun Wang, Bian...