8:25 - 8:30

Welcome

Prasad Tadepalli, Robert Givan, and Kurt Driessens

8:30 - 9:00

Relational Regression and Q-learning

Kurt Driessens

9:00 - 9:15

Discussion

9:15 - 9:30

On the Numeric Stability of Gaussian Processes Regression for Relational Reinforcement Learning

Jan Ramon and Kurt Driessens

9:30 - 9:45

Relational Reinforcement Learning via Sampling the Space of First-Order Conjunctive Features

Trevor Walker, Jude Shavlik, and Richard Maclin

9:45 - 10:00

Discussion

10:00 - 10:30

Coffee Break

10:30 - 11:00

Relational Reinforcement Learning for Classical Planning

Alan Fern, SungWook Yoon, and Robert Givan

11:00 - 11:15

Discussion

11:15 - 11:30

Towards Learning to Learn and Plan by Relational Reinforcement Learning

Hideaki Itoh and Kiyohiko Nakamura

11:30 - 11:45

Towards Informed Reinforcement Learning

Tom Croonenborghs, Jan Ramon, and Maurice Bruynooghe

11:45 - 12

Discussion

12:00 - 2:00

Lunch break

2:00 - 2:30

Exploiting First-Order Regression in Inductive Policy Selection

Charles Gretton and Sylvie Thiebaux

2:30 - 2:45

Discussion

2:45 - 3:00

Soar-RL: Integrating Reinforcement Learning with Soar

Shelley Nason and John E. Laird

3:00 - 3:15

Model-Based Learning with Hierarchical Relational Skills

Pat Langley, Sachiyo Arai, and Daniel Shapiro

3:15 - 3:30

Discussion

3:30 - 4:00

Coffee Break

4:00 - 4:15

Relational State Abstractions for Reinforcement Learning

Eduardo F. Morales

4:15 - 4:30

Challenges for Relational Reinforcement Learning

Martijn Van Otterlo and Kristian Kersting

4:30 - 4:45

Discussion

4:45 - 5:30

Panel

Luc De Raedt, Roni Khardon, Stuart Russell, Jude Shavlik, and  Malcolm Strens