(Enter summary)
Abstract: This paper investigates an approach to designing
and building adaptive agents. The main contribution is the use of
a symbolic machine learning system for approximating the policy
and Q functions that are at the heart of the agent. Under the
assumption that sufficient knowledge of the application domain
is available, it is shown how this knowledge can be provided
to the agent in the form of symbolic hypothesis languages for
the policy and Q functions, and the advantages of such an
approach. A... (Update)
Cited by: More
Exploiting First-Order Regression in Inductive Policy.. - Gretton, Thiébaux (2004)
(Correct)
Active bibliography (related documents): More All
0.3: Logical Markov Decision Programs - Kersting, De Raedt (2003)
(Correct)
0.2: Speeding up Relational Reinforcement Learning Through.. - Driessens, Ramon.. (2001)
(Correct)
0.2: Using ILP to Improve Planning in Hierarchical Reinforcement.. - Reid, Ryan (2000)
(Correct)
Similar documents based on text: More All
0.6: Draft June 2004 - Alkemy Learning System
(Correct)
0.6: August 2004 - Alkemy Learning System
(Correct)
0.1: Classification of Individuals with Complex Structure - Bowers, Giraud-Carrier, Lloyd (2000)
(Correct)
BibTeX entry: (Update)
J. Cole, J.W. Lloyd, and K.S. Ng. Symbolic Learning for Adaptive Agents. In Proc. Annual Partner Conference, Smart Internet Technology Cooperative Research Centre, 2003. http://csl.anu.edu.au/ jwl/crc paper.pdf http://citeseer.ist.psu.edu/cole03symbolic.html More
@misc{ cole03symbolic,
author = "J. Cole and J. Lloyd and K. Ng",
title = "Symbolic Learning for Adaptive Agents",
text = "J. Cole, J.W. Lloyd, and K.S. Ng. Symbolic Learning for Adaptive Agents.
In Proc. Annual Partner Conference, Smart Internet Technology Cooperative
Research Centre, 2003. http://csl.anu.edu.au/ jwl/crc paper.pdf",
year = "2003",
url = "citeseer.ist.psu.edu/cole03symbolic.html" }
Citations (may not include all citations):
614
Reinforcement Learning: An Introduction
- Sutton, Barto - 1998
413
Neuro-dynamic Programming (context) - Bertsekas, Tsitsiklis - 1996
291
Markov Decision Processes: Discrete Stochastic Dynamic Progr.. (context) - Puterman - 1994
281
Machine Learning (context) - Watkins - 1992
261
Reactive reasoning and planning (context) - Georgeff, Lansky - 1987
250
Artificial Intelligence: A Modern Approach (context) - Russell, Norvig - 2002
188
Decision theoretic planning: Structural assumptions and comp..
- Boutilier, Dean et al. - 1999
14
Blocks world revisited (context) - Slaney, ebaux - 2001
9
Relational reinforcement learning (context) - zeroski, De Raedt et al. - 2001
9
Relational reinforcement learning (context) - zeroski, De Raedt et al. - 1998
1
Incremental induction of Alkemic trees (context) - Ng - 2003
1
Cognitive Technologies (context) - Lloyd, Learning - 2003
http://csl.anu.edu.au/kee/Alkemy
Documents on the same site (http://discus.anu.edu.au/~kee/index.html): More
Evolutionary Learning on Structured Data for Artificial Neural.. - Radlinski
(Correct)
Solving the Musk Problem with Alkemy - Kee Siong Ng
(Correct)
Draft June 2004 - Alkemy Learning System
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC