Kee Siong's Homepage
Kee Siong Ng
Adjunct Research Fellow,
Computer Sciences Laboratory,
College of Engineering and Computer Science,
The Australian National University.
keesiong dot ng at nicta dot com dot au
I'm now with the Making Sense of Data (NRL) group
in
NICTA.
Softwares
Download the latest version of
Alkemy.
(Escher is now incorported into Alkemy!!)
Download the latest version of
Escher!
The Godel programming language can be found
here.
I'm currently working on Bach. This is a work-in-progress, but a
version of the system is available for experimentation on request.
Here's the literate program
An Implementation of Bach.
Datasets and Useful Documents
Papers
Journal papers
- Probabilistic Modelling, Inference and Learning using Logical
Theories
K.S. Ng, J.W. Lloyd, W.T.B. Uther, Annals of Mathematics and
Artificial Intelligence, 2009, To appear.
- Probabilistic Reasoning in a Classical Logic
K.S. Ng, J.W. Lloyd, Journal of Applied Logic, 2008, To appear.
Conference/Workshop papers
- Probabilistic and Logical Beliefs
J.W. Lloyd, K.S. Ng, In M. Dastani et al (Eds), LADS 2007, LNAI
5118, pp. 19-36, 2008.
- Reflections on Agent Beliefs
J.W. Lloyd, K.S. Ng, In M. Baldoni et al (Eds), DALT 2007, LNCS
4897, pp. 122-139, 2007.
- Learning Modal Theories
J.W. Lloyd, K.S. Ng, In S. Muggleton, R. Otero and
A. Tamaddoni-Nezhad (Eds.): ILP 2006, LNAI 4455, pp. 320-334, 2007.
- This paper presents a general framework for learning theories in a
higher-order multi-modal logic.
- (Agnostic) PAC Learning Concepts in
Higher-order Logic, (A longer preprint.)
K.S. Ng, In J. Furnkranz, T. Scheffer and M. Spiliopoulou (Eds.):
ECML 2006, LNAI 4212, pp. 711-718, 2006.
- This paper studies the PAC and agnostic PAC learnability of some
function classes expressible in higher-order logic.
- Generalization Behaviour of Alkemic Decision Trees,
K.S. Ng,
In S. Kramer and B. Pfahringer (Eds.): ILP 2005, LNAI 3625,
pp. 246-263, 2005.
- This paper studies the VC dimensions of some common function
classes defined on structured data, including sets, multisets,
trees, graphs, etc.
- Predicate Selection for
Structural Decision Trees, Additional notes
K.S. Ng, J.W. Lloyd,
In S. Kramer and B. Pfahringer (Eds.): ILP 2005, LNAI 3625,
pp. 264-278, 2005.
- This paper gives efficient algorithms for the problem of
picking from a structured search space a predicate that partitions
a set of examples well.
- Personalisation for User Agents
J.J.Cole, M.Gray, J.W. Lloyd, K.S. Ng,
In Proceedings of the 4th International Joint
Conference on Autonomous Agents and Multi Agent Systems
(AAMAS-05), pp. 603-610,
2005.
- This paper presents a symbolic machine learning framework for
achieving personalisation in intelligent user agents.
- Symbolic Learning for Adaptive Agents
J.J. Cole, J.W. Lloyd, K.S. Ng, Proceedings of the Annual Partner
Conference, Smart Internet Technology Cooperative Research Centre,
pp 139--148, 2003
- This paper presents an interesting new perspective on relational
reinforcement learning.
- Predictive Toxicology using a Decision-tree
Learner
K.S. Ng, J.W. Lloyd, A.W. Slater, The 2000-1 Predictive
Toxicology Challenge Workshop, 5th European Conference on Principles
and Practice of Knowledge Discovery in Databases (PKDD-01), 2001
Theses
Miscellaneous