Sciweavers

AAAI
2008
14 years 8 hour ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
AAAI
2007
14 years 8 hour ago
Authorial Idioms for Target Distributions in TTD-MDPs
In designing Markov Decision Processes (MDP), one must define the world, its dynamics, a set of actions, and a reward function. MDPs are often applied in situations where there i...
David L. Roberts, Sooraj Bhat, Kenneth St. Clair, ...
AAAI
2008
14 years 8 hour ago
Constraint Projections for Ensemble Learning
It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through res...
Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou, Qiang ...
AAAI
2007
14 years 8 hour ago
Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
AAAI
2008
14 years 8 hour ago
Revising Imprecise Probabilistic Beliefs in the Framework of Probabilistic Logic Programming
Probabilistic logic programming is a powerful technique to represent and reason with imprecise probabilistic knowledge. A probabilistic logic program (PLP) is a knowledge base whi...
Anbu Yue, Weiru Liu
AAAI
2007
14 years 8 hour ago
Cost-Sensitive Imputing Missing Values with Ordering
Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with o...
Xiaofeng Zhu, Shichao Zhang, Jilian Zhang, Chengqi...
AAAI
2007
14 years 8 hour ago
Robust Estimation of Google Counts for Social Network Extraction
Yutaka Matsuo, Hironori Tomobe, Takuichi Nishimura