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

I am 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 principal data scientist with EMC Greenplum.

I also have three US$2.56 cheques from Prof Don Knuth. :)

- 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

- The MADlib Analytics Library or MAD Skills, the SQL

J.M. Hellerstein, C. Re, F. Schoppmann, Z.D. Wang, E. Fratkin, A, Gorajek, K.S. Ng, C. Welton, X. Feng, K. Li, A. Kumar, In PVLDB, 2012.

- 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.

- 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.

- This paper describes the computational model of the Bach programming language.
The design of Bach builds on
- Probabilistic Reasoning in a Classical Logic

K.S. Ng, J.W. Lloyd, Journal of Applied Logic, vol 7, pp. 218-238, 2009.

- Consistently in the top five most downloaded papers at the JAL since publication; see Top 25 at ScienceDirect.

- 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.

- This paper describes how different forms of probabilistic
modelling, inference, and learning described in the

- Predicting High Impact Academic Papers Using Citation Network Features

D. McNamara, P. Wong, P. Christen, K.S. Ng, In DMApps, 2013.

- 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

- Learning Comprehensible Theories from
Structured Data,

K.S. Ng, PhD Thesis, The Australian National University, 2005. (postscript version)

- Neural Networks for Structured
Data

K.S. Ng, Honours Thesis, 2001

- A Simple Explanation of Partial Least Squares

K.S. Ng, 2013 - Approximate Inference in Structured Bayesian
Networks

R. Kumar, K.S. Ng, 2009 - Modal Functional Logic Programming

J.W. Lloyd, K.S. Ng, J.W. Veness, Tech Report, 2007 - Agnostic PAC Learning Decision Lists is Hard

K.S. Ng, 2005 - Generalization Bounds for Structural
Decision Trees,

K.S. Ng, 2005

- Towards General Machine Intelligence, K.S. Ng, July 2009.

- Architectures for Intelligent Agents, K.S. Ng, W. Uther, B. Hengst, Sep 2007

Download the latest version of Alkemy. (Escher is now incorported into Alkemy!!)

- This is the manual for Alkemy.

A Tutorial Introduction to Alkemy K.S. Ng, 2006

- This is the Alkemy literate program.

The Alkemy Source Book K.S. Ng, 2009, 258 pages.

- This is the Escher literate program.

An Implementation of Escher K.S. Ng, 2006.

Bach 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.

- PC Member for the 2013 AAAI Workshop on Statistical Relational Artificial Intelligence
- PC Member for the 2012 ICML Workshop on Statistical Relational Learning
- Member of the Leadership Group for the ACT Chapter of the Analyst First Society
- PC Member for the Solomonoff 85th Memorial Conference
- Co-organiser of a NIPS-2009 mini symposium on Partially Observable Reinforcement Learning
- NICTA Workshop on Integrating Logic and Probability, 2009
- PC Member for 2009 International Workshop on Statistical Relational Learning (SRL-09)
- PC Member for 2006 European Conference on Machine Learning (ECML-06)