Kee Siong Ng
Research School of Computer Science,
College of Engineering and Computer Science,
The Australian National University.
keesiong dot ng at gmail dot com
Brief Bio
I am originally from a small town called Muar in Malaysia.
I came to Australia in 1998 to study and have been here since.
I am currently involved in several aspects of artificial intelligence research, including attempts on approximating the universal
AIXI agent.
I spent most of my time working as a data scientist with EMC Greenplum.
I also have three US$2.56 cheques from Prof Don Knuth. :)
Professional Interests
- In-Database Machine Learning
- Integrating logic and probability
- Symbolic or knowledge-based machine learning
- Functional and logic programming paradigms
- Automated theorem proving in higher-order logics
- Uncertainty modelling in AI
- Universal reinforcement learning
- Computational learning theory
- Literate programming
Publications
Journal papers
- Probabilities on Sentences in an Expressive Logic
M. Hutter, J.W. Lloyd, K.S. Ng, W. Uther, Progic 2011, Journal of Applied Logic, To appear
- A Monte
Carlo AIXI Approximation
J. Veness, K.S. Ng, M. Hutter, W. Uther, D. Silver,
Journal of Artificial Intelligence Research, vol 40, pp. 95-142, 2011
- This paper describes a computationally feasible approximation to the universal reinforcement
learning agent AIXI.
- Declarative Programming for Agent Applications
J.W. Lloyd, K.S. Ng, Journal of Autonomous Agents and
Multi-Agent Systems, vol 23(2), pp. 224-272, 2011.
- This paper describes the computational model of the Bach programming language.
The design of Bach builds on
the lessons learned from the design
of the Godel and
Escher programming languages.
- Probabilistic Reasoning in a Classical Logic
K.S. Ng, J.W. Lloyd, Journal of Applied Logic, vol 7,
pp. 218-238, 2009.
- Probabilistic Modelling, Inference and Learning using Logical
Theories
K.S. Ng, J.W. Lloyd, W.T.B. Uther, Annals of Mathematics and
Artificial Intelligence, vol 54(1), pp. 159-205, 2008.
- This paper describes how different forms of probabilistic
modelling, inference, and learning described in the
probabilistic logics literature
can be elegantly and simply handled in a standard classical logic:
higher-order logic.
The problem of doing approximate inference in this logic, which
was open at the time of publication, has now been
partially resolved; see Ramana Kumar's project report.
Conference/Workshop papers
- Context Tree Switching
J. Veness, K.S. Ng, M. Hutter, M. Bowling,
In IEEE Data Compression Conference, 2012.
- Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia
K.S. Ng, Y. Shan, D.W. Murray, A. Sutinen, B. Schwarz, D. Jeacocke, J. Farrugia,
In DMAGI, ICDM Workshops 2010, pp. 613-622, 2010.
- Reinforcement Learning via AIXI Approximation
J. Veness, K.S. Ng, M. Hutter, D. Silver, AAAI 2010, pp. 605-611.
- Probabilistic and Logical Beliefs
J.W. Lloyd, K.S. Ng, In M. Dastani et al (Eds), LADS 2007, LNAI
5118, pp. 19-36, 2008.
- This paper argues for a unified treatment of the notion of
beliefs as traditionally used in probabilistic robotics and
logic-based BDI agents.
- Reflections on Agent Beliefs
J.W. Lloyd, K.S. Ng, In M. Baldoni et al (Eds), DALT 2007, LNCS
4897, pp. 122-139, 2007.
- This paper provides some reflections on the form beliefs
should take in logic-based agents that both reason and learn.
- 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
Tech Reports
Project Proposals
- Towards General Machine Intelligence, K.S. Ng, July 2009.
- Architectures for Intelligent Agents, K.S. Ng, W. Uther,
B. Hengst, Sep 2007
Softwares
As part of Greenplum, I contribute to the open-source MADlib project,
which seeks to foster innovation in large-scale in-database analytics.
Download the latest version of Alkemy.
(Escher is now incorported into Alkemy!!)
Download the latest version of
Escher! See also
the Wikipedia
entry on 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.
Community Service
Datasets and Useful Documents