Son Bao Pham

Omnidirectional Locomotion for Quadruped Robots

Competing at the RoboCup 2000 Sony legged robot league, the UNSW team won both the challenge competition and all their soccer matches, emerging the outright winners for this league against eleven other international teams. The main advantage that the UNSW team had was speed. A major contributor to the speed was a novel omnidirectional locomotion method developed for the quadruped Sony ERS-110 robot used in the competition. It is believed to be the fastest walk style known for this type of robot. In this paper we describe the parameterised omnidirectional walk in detail. The walk also made a positive contribution to other robot tasks such as ball tracking and localisation while playing soccer. The authors believe that this omnidirectional locomotion could be applied more generally in other legged robots.

A New Approach for Scientific Citation Classification Using Cue Phrases

This paper introduces a new method for the rapid development of complex rule bases involving cue phrases for the purpose of classifying text segments. The method is based on Ripple-Down Rules, a knowledge acquisition method that proved very successful in practice for building medical expert systems and does not require a knowledge engineer. We implemented our system KAFTAN and demonstrate the applicability of our method to the task of classifying scientific citations. Building cue phrase rules in KAFTAN is easy and efficient. We demonstrate the effectiveness of our approach by presenting experimental results where our resulting classifier clearly outperforms previously built classifiers in the recent literature.

The UNSW RoboCup 2000 Sony Legged League Team

We describe our technical approach in competing at the RoboCup 2000 Sony legged robot league. The UNSW team won both the challenge competition and all their soccer matches, emerging the outright winners for this league against eleven other international teams. The main advantage that the UNSW team had was speed. The robots not only moved quickly, due to a novel locomotion method, but they also were able to localise and decide on an appropriate action quickly and reliably. This report describes the individual software sub-systems and software architecture employed by the team.

Extracting Positive Attributions from Scientific Papers

The aim of our work is to provide support for reading (or skimming) scientific papers. In this paper we report on the task to identify concepts or terms with positive attributions in scientific papers. This task is challenging as it requires the analysis of the relationship between a concept or term and its sentiment expression. Furthermore, the context of the expression needs to be inspected. We propose an incremental knowledge acquisition framework to tackle these challenges. With our framework we could rapidly (within 2 days of an expert’s time) develop a prototype system to identify positive attributions in scientific papers. The resulting system achieves high precision (above 74%) and high recall rates (above 88%) in our initial experiments on corpora of scientific papers. It also drastically outperforms baseline machine learning algorithms trained on the same data.

Towards topic-based summarization for interactive document viewing

Our research aims at interactive document viewers that can select and highlight relevant text passages on demand. Another related objective is the generation of topic-specific summaries of texts as opposed to general purpose summaries. This paper introduces our notions of discourse structure tree and level-of-detail tree. Both structures are used to represent relevant aspects of a text segment for the above mentioned purposes. Furthermore, we introduce a Knowledge Acquisition Framework for DIScourse processing (KAFDIS) that allows the incremental and efficient acquisition of knowledge for the reliable construction of the discourse structure graph and the level-of-detail tree based on cue phrases. An effective knowledge acquisition process is crucial to allow the economical development of systems that can handle a large variety of topics. Our knowledge acquisition approach ensures always a consistent knowledge base whose semantics are well controlled by the expert. It is an incremental approach that allows patching of the knowledge base as soon as the need arises without causing any inconsistencies. We also present promising experimental results with our approach.

The UNSW RoboCup 2001 Sony Legged Robot League Team

In 2001, the UNSW United team in the Sony legged robot league successfully defended its title. While the main effort in last years competition was to develop sound low-level skills, this years team focussed primarily on experimenting with new behaviours. An important part of the teams preparation was playing practice matches in which the behaviour of the robots could be studied under actual game-play conditions. In this paper, we describe the evolution of the software from previous years and the new skills displayed by the robots.

Incremental Knowledge Acquisition for Building Sophisticated Information Extraction Systems with KAFTIE

The aim of our work is to develop a flexible and powerful Knowledge Acquisition framework that allows users to rapidly develop Natural Language Processing systems, including information extraction systems. In this paper we present our knowledge acquisition framework, KAFTIE, which strongly supports the rapid development of complex knowledge bases for information extraction. We specifically target scientific papers which involve rather complex sentence structures from which different types of information are automatically extracted. Tasks on which we experimented with our framework are to identify concepts/terms of which positive or negative aspects are mentioned in scientific papers. These tasks are challenging as they require the analysis of the relationship between the concept/term and its sentiment expression. Furthermore, the context of the expression needs to be inspected. The results so far are very promising as we managed to build systems with relative ease that achieve F-measures of around 84% on a corpus of scientific papers in the area of artificial intelligence.

Incremental knowledge acquisition for extracting temporal relations

We present KAFTIE - an incremental knowledge acquisition framework which utilizes expert knowledge to build high quality knowledge base annotators. Using KAFTIE, a knowledge base was built based on a small data set that outperforms machine learning algorithms trained on a much bigger data set for the task of recognizing temporal relations. In particular, this can be incorporated to bootstrap the process of labeling data for domains where annotated data is not available.

Stochastic Gradient Descent Localisation in Quadruped Robots

Competing at the RoboCup 2000 Sony legged robot league, the UNSW team convincingly won all their matches. One of the advantages of the team was a new localisation algorithm that is very fast and can tolerate noisy input from the environment as well as unexpected collisions with other objects. This paper describes the algorithm in detail.

KAFTIE: A New KA Framework for Building Sophisticated Information Extraction Systems

The aim of our work is to develop a flexible and powerful Knowledge Acquisition framework that allows users to rapidly develop Natural Language Processing systems, including information extraction systems. Tasks on which we experimented with our framework are to identify concepts/terms of which positive or negative aspects are mentioned in scientific papers. The results so far are very promising as we managed to build systems with relative ease that achieve F-measures of around 84% on a corpus of scientific papers in the area of artificial intelligence.