Claude Sammut

Extracting Hidden Context

Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and communication network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden changes in context as they assume that the training set is homogeneous. An off-line, meta-learning approach for the identification of hidden context is presented. The new approach uses an existing batch learner and the process of contextual clustering to identify stable hidden contexts and the associated context specific, locally stable concepts. The approach is broadly applicable to the extraction of context reflected in time and spatial attributes. Several algorithms for the approach are presented and evaluated. A successful application of the approach to a complex flight simulator control task is also presented.

Majority Voting: Material Classification by Tactile Sensing Using Surface Texture

In this paper, we present an application of machine learning to distinguish between different materials based on their surface texture. Such a system can be used for the estimation of surface friction during manipulation tasks; quality assurance in the textile, cosmetics, and harvesting industries; and other applications requiring tactile sensing. Several machine learning algorithms, such as naive Bayes, decision trees, and naive Bayes trees, have been trained to distinguish textures sensed by a biologically inspired artificial finger. The finger has randomly distributed strain gauges and polyvinylidene fluoride (PVDF) films embedded in silicone. Different textures induce different intensities of vibrations in the silicone. Consequently, textures can be distinguished by the presence of different frequencies in the signal. The data from the finger are preprocessed, and the Fourier coefficients of the sensor outputs are used to train classifiers. We show that the classifiers generalize well for unseen datasets with performance exceeding previously reported algorithms. Our classifiers can distinguish between different materials, such as carpet, flooring vinyls, tiles, sponge, wood, and polyvinyl-chloride (PVC) woven mesh with an accuracy of on unseen test data.

Classification of Multivariate Time Series and Structured Data Using Constructive Induction

We present a method of constructive induction aimed at learning tasks involving multivariate time series data. Using metafeatures, the scope of attribute-value learning is expanded to domains with instances that have some kind of recurring substructure, such as strokes in handwriting recognition, or local maxima in time series data. The types of substructures are defined by the user, but are extracted automatically and are used to construct attributes.Metafeatures are applied to two real domains: sign language recognition and ECG classification. Using metafeatures we are able to generate classifiers that are either comprehensible or accurate, producing results that are comparable to hand-crafted preprocessing and comparable to human experts.

Effective user interface design for rescue robotics

Until robots are able to autonomously navigate, carry out a mission and report back to base, effective human-robot interfaces will be an integral part of any practical mobile robot system. This is especially the case for robot-assisted Urban Search and Rescue (USAR). Unfamiliar and unstructured environments, unreliable communications and many sensors combine to make the job of a human operator, and hence the interface designer challenging.This paper presents the design, implementation and deployment of a human-robot interface for the teleoperated USAR research robot, textsfCASTER. Proven HCI-based user interface design principles were adopted in order to produce an interface that was intuitive and minimised learning time while maximising effectiveness.The human-robot interface was deployed by Team CASualty in the 2005 RoboCup Rescue Robot League competition. This competition allows a wide variety of approaches to USAR research to be evaluated in a realistic environment. Despite the operator having less than one month of experience, Team CASualty came 3rd, beating teams that had far longer to train their operators. In particular, the ease with which the robot could be driven and high quality information gathered played a crucial part in Team CASualtys success. Further empirical evaluations of the system on a group of twelve users as well as members of the public further reinforce our belief that this interface is quick to learn, easy to use and effective.

Incremental Learning of Linear Model Trees

A linear model tree is a decision tree with a linear functional model in each leaf. Previous model tree induction algorithms have been batch techniques that operate on the entire training set. However there are many situations when an incremental learner is advantageous. In this article a new batch model tree learner is described with two alternative splitting rules and a stopping rule. An incremental algorithm is then developed that has many similarities with the batch version but is able to process examples one at a time. An online pruning rule is also developed. The incremental training time for an example is shown to only depend on the height of the tree induced so far, and not on the number of previous examples. The algorithms are evaluated empirically on a number of standard datasets, a simple test function and three dynamic domains ranging from a simple pendulum to a complex 13 dimensional flight simulator. The new batch algorithm is compared with the most recent batch model tree algorithms and is seen to perform favourably overall. The new incremental model tree learner compares well with an alternative online function approximator. In addition it can sometimes perform almost as well as the batch model tree algorithms, highlighting the effectiveness of the incremental implementation.

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.

Material classification by tactile sensing using surface textures

In this paper we describe an application of machine learning to distinguish between seven different materials, based on their surface texture. Applications of such a system includes quality assurance and estimating surface friction during manipulation tasks. A naive Bayes classifier is used to distinguish textures sensed by a bio-inspired artificial finger. The finger has randomly distributed strain gauges and Polyvinylidene Fluoride (PVDF) films embedded in silicone. Different textures induce different intensity of vibrations in the silicone. Textures can be distinguished by the presence of different frequencies in the signal. The data from the finger is pre-processed and the Fourier coefficients of the sensor outputs are used to learn a classifier for different textures. The performance of the classifier is evaluated against a naive time domain based learner. Preliminary results show that our classifier performs better.

Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems

This paper reports on experiments performed with a variety of algorithms that have been used for the task of learning to control dynamic systems. It compares their speed, reliability and the assumptions about the problem domain which must be made in order for them to work. We describe a promising new method which combines induction with reinforcement learning. The output of this method is a set of control rules which are fast, reliable and, most importantly, more readable than the parameters and weights which constitute the knowledge of a pure reinforcement system. Finally, some open questions are presented.

Automatic construction of reactive control systems using symbolic machine learning

This paper reviews a number of applications of machine learning to industrial control problems. We take the point of view of trying to automatically build rule-based reactive systems for tasks that, if performed by humans, would require a high degree of skill, yet are generally performed without thinking. Such skills are said to be sub-cognitive. While this might seem restrictive, most human skill is executed subconsciously and only becomes conscious when an unfamiliar circumstance is encountered. This kind of skill lends itself well to representation by a reactive system, that is, one that does not create a detailed model of the world, but rather, attempts to match percepts with actions in a very direct manner.

Learning to fly by combining reinforcement learning with behavioural cloning

Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficult to solve with existing reinforcement learning methods due to the large search space. In this paper, we use a relational representation to define powerful abstractions that allow us to incorporate domain knowledge and re-use previously learned policies in other similar problems. We also describe how to learn useful actions from human traces using a behavioural cloning approach combined with an exploration phase. Since several conflicting actions may be induced for the same abstract state, reinforcement learning is used to learn an optimal policy over this reduced space. It is shown experimentally how a combination of behavioural cloning and reinforcement learning using a relational representation is powerful enough to learn how to fly an aircraft through different points in space and different turbulence conditions.