No description available.

In this thesis we develop and test a software algorithm for an electronic smart badge system. The smart badge system we have developed has the ability to figure out the interests of people who wear the badge by using time and position information collected by the badge. The badge can also present feedback to the wearer, so that users may be guided to people will similar interests and so may have more effective conversations. The smart badge system is based on an inference system which uses a Bayesian network. Evaluation of the system was challenging because there were no completed badges that could be used. To overcome this, we developed a simulation of crowd behaviour in a conference setting. We tuned the parameters of the model using several test situations and the final simulated behavior appeared realistic. Compared to other smart badge systems, our work is unique because it is able to enhance conversation by the real time inference of common ideas or interests of the conversion participants.

Programming is one of the core skills required by Computer Science undergraduates in tertiary institutions worldwide, whether for study itself, or to be used as a tool to explore other relevant areas. Unfortunately, programming can be incredibly difficult; this is for several reasons, including the youth, depth, and variety of the field, as well as the youth of the technology that frames it. It can be especially problematic for computing neophytes, with some students repeating programming courses not due to academic laziness, but due to an inability to grasp the core concepts. The research outlined by this thesis focuses on our proposed solution to this problem, a constraint-based intelligent tutoring system for teaching the Java programming language, named J-LATTE. J-LATTE (Java Language Acquisition Tile Tutoring Environment) is designed to solve this problem by providing a problem-solving environment for students to work through programming problems. This environment is unique in that it partitions interaction into a concept mode and a coding mode. Concept mode allows the student to form solutions using high-level Java concepts (in the form of tiles), and coding mode allows the student to enter Java code into these tiles to form a complete Java program. The student can, at any time, ask for feedback on a solution or partial solution that they have formed. A pilot study and two full evaluations were carried out to test the effectiveness of the system. The pilot study was run with an assignment given to a postgraduate Computer Science course, and because of the advanced knowledge level of the students, it was not designed to test teaching effectiveness, but instead was useful in determining usability issues and identifying any software errors. The full evaluations of the system were designed to give insight into the teaching effectiveness of J-LATTE, by comparing the results of using the system against a simulated classroom situation. Unfortunately, the participant base was small, for several reasons that are explained in the thesis. However, the results prove interesting otherwise and for the most part are positive towards the effectiveness of J-LATTE. The participants’ knowledge did improve while interacting with the system, and the subjective data collected shows that students like the interaction style and value the feedback obtained.

Intensive Care depends on sophisticated life support technology. Effective management of device-supported patients is complex, involving the interpretation of many variables, comparative evaluation of numerous therapy options, and control of various patient-management parameters. Raw data, when taken literally, can lead to the wrong interpretation of the patient. We propose a system which processes raw data in real-time for intelligent alarming and analyses historical data for summarisation and patient state assessment. This will utilise a temporal expert system which incorporates associational reasoning. Using continuous physiological data from monitors, patient history and times of therapy administration, our research consists of applying three consecutive processes: <I>filtering </I>which is used to remove noise in the physiological data; <I>interval identification </I>which generates temporal intervals from the filtered data points which have abstractions relating to their direction of change (i.e. increasing, decreasing and steady); and <I>interpretation</I> which performs summarisation and patient state-assessments from a historical point of view and intelligent alarming from a real-time point of view. Using the temporal intervals, interpretation involves differentiating between events which are clinically insignificant and events which are clinically significant. We need to identify and remove clinically insignificant events (e.g. line flushes, blood samples etc.). Similarly, we need to identify clinically significant events i.e. clinical conditions (e.g. hypovolaemia, pulmonary haemorrhage etc.) and the outcome of therapies - this will utilise the patient history and times of therapy administration. Inherent in this process is the <I>trend template </I>which is used to represent events. Trend templates support temporal reasoning, knowledge to differentiate between events and taxonomical knowledge. Algorithms which are analogous to the way clinicians identify events use these trend templates.

No description available.

The goal of this thesis is to develop an intuitive, adaptive, and flexible architecture for controlling intelligent mobile robots. This architecture is a hybrid architecture that combines deliberative planning, reactive control, finite state automata, behaviour trees and uses competition for behaviour selection. This behaviour selection is based on a task manager, which selects behaviours based on approximations of their applicability to the current situation and the expected reward value for performing that behaviour. One important feature of this architecture is that it makes important behavioural information explicit using Extensible Markup Language (XML). This explicit representation is an important part in making the architecture easy to debug and extend. The utility, intuitiveness and flexibility of this architecture is shown in an evaluation of this architecture against older control programs that lack such explicit behavioural representation. This evaluation was carried out by developing behaviours for several common robotic tasks and demonstrating common problems that arose during the course of this development.

No description available.

No description available.

No description available.

The goal of this thesis is to develop an intuitive, adaptive, and flexible architecture for controlling intelligent mobile robots. This architecture is a hybrid architecture that combines deliberative planning, reactive control, finite state automata, behaviour trees and uses competition for behaviour selection. This behaviour selection is based on a task manager, which selects behaviours based on approximations of their applicability to the current situation and the expected reward value for performing that behaviour. One important feature of this architecture is that it makes important behavioural information explicit using Extensible Markup Language (XML). This explicit representation is an important part in making the architecture easy to debug and extend. The utility, intuitiveness and flexibility of this architecture is shown in an evaluation of this architecture against older control programs that lack such explicit behavioural representation. This evaluation was carried out by developing behaviours for several common robotic tasks and demonstrating common problems that arose during the course of this development.