Arnold Depickere

In this paper, we examine the use of knowledge query technology as applied to conversation bots in the instant messaging environment. Hence, we designed an artificial intelligent conversation robot or bots called Artificial Intelligence Natural language Identity (hereafter, AINI) to mimic human conversation. Our goal is to introduce a Domain Matrix Knowledge Model and an Automated Knowledge Extraction Agent (AKEA) to create AINI's knowledge bases, and in turn provide intelligent query mechanisms. We report an evaluation on the collection and analysis of a corpus containing over 3280 utterances in a series of real instant messages exchanged between the AINI conversation bot and 65 online buddies. About 1721 utterances were produced by AINI, 88.03% were from open-domain knowledge, 2.15% from domain-specific knowledge base and 9.82% were inappropriate and amusing responses. These results show that domain knowledge plays significant roles in conversations between two or more human users and in human-machine conversation.

The main aim of this research is to investigate a novel technique for implementing a conversational intelligent agent system on the web. The research will focus on a multilevel natural language query approach. While the present World Wide Web provides a distributed hypermedia interface to the vast amount of information on the Internet, there is a lack of appropriate metadata to that content. Instead of being a giant library as intended, increasing sections of the Web resemble a giant garbage dump. A multilevel natural language query system is described in this paper for the representation of knowledge in specific and open domains. The six-layer conversational agent system includes Spellcheck, Natural Language Understanding and Reasoning, FAQChat, Metadata Index Search, Pattern Matching and Case-Based Reasoning, and Supervised Learning. This technique is demonstrated using extracts from queries on the field of data relating to the Bird Flu H5N1 pandemic crisis.

This paper presents a novel framework for modeling embodied conversational agent for crisis communication focusing on the H5N1 pandemic crisis. Our system aims to cope with the most challenging issue on the maintenance of an engaging while convincing conversation. What primarily distinguishes our system from other conversational agent systems is that the human-computer conversation takes place within the context of H5N1 pandemic crisis. A Crisis Communication Network, called CCNet, is established based on a novel algorithm incorporating natural language query and embodied conversation agent simultaneously. Another significant contribution of our work is the development of a Automated Knowledge Extraction Agent (AKEA) to capitalize on the tremendous amount of data that is now available online to support our experiments. What makes our system differs from typical conversational agents is the attempt to move away from strictly task-oriented dialogue.

Universities teach project management to information technology (IT) students. The project management principles that students previously have learned often are put into practice in a project course that is intended to give final-year students the experience of applying their knowledge to real or simulated projects. This article reports on research that investigated the use and usefulness of project management in student IT projects. The results show that there was a wide range in the application of project management practices, with students being more likely to produce the initial documentation associated with some of the project management knowledge areas than to make use of it throughout the project to monitor the project’s progress. The results also showed that the number of project management guidelines applied in student projects was not linked to IT project success. However, there was a strong relationship between project management plan quality and obtaining a good software product.

In this paper, we proposed a new framework to incorporate an intelligent agent software robot into a crisis communication portal (CCNet) in order to send alert news to subscribed users via email and other mobile services such as Short Message Service (SMS), Multimedia Messaging Service (MMS) and General Packet Radio Services (GPRS). The content on the mobile services can be delivered either through mobile phone or Personal Digital Assistance (PDA). This research has shown that with our proposed framework, the embodied conversation agents system can handle questions intelligently with our multilayer architecture. At the same time, the extended framework can take care of delivery content through a more humanoid interface on mobile devices.

Recent developments in learning technology such as hypermedia is becoming widespread and offer significant contribution to improve the delivery of learning and teaching materials. A key factor in the development of hypermedia learning system is cognitive style (CS) as it relates to users' information processing habits, representing individual user's typical modes of perceiving, thinking, remembering and problem solving. The sample comprised of 217 students from Murdoch University who were enrolled in a first-year undergraduate unit. A survey was carried out every second semester over a period of 3 years (1999-2001). Both generalized linear model and tree-based regression were used to analyse the interaction among the learning dimensions and the effect on students' CS. When comparing both models, tree-based regression outperformed generalized linear model in this study. The research findings indicated that non-linear learning is the primary dimension that determines students' CS. This is subsequently, followed by multiple tools (MT) and learner control (LC) dimensions. The results also confirm that background information has effects on students' CS. The overall findings suggest that students' preference of learning dimensions such as linear vs. non-linear, level of LC and the range of MT must be taken into consideration in order to enrich students' quality of education by means of motivating students' acquisition of subject matter through individualize instruction when designing, developing, and delivering educational resources.

College and university environments offer one of the best opportunities for the adoption of smart card technology. This study explores the possibility of developing smart card as a university application and investigates whether it will help to reduce the university business administration procedures as well as increase their service efficiency. This involved investigating the response of two groups (experienced and non-experienced) towards the adoption or the intention to embrace the technology of a university smart card. Studying the experiences gained by Nanyang Technological University (NTU, Singapore) students using smart card application in their university may provide an insightful picture for a university that is considering adopting such a technological innovation. Results show that compatibility pre-adoption construct is the key element that plays the most influential factor in motivating local students for adoption. This suggests that to ensure adoption, it is important to guarantee that the university smart cards are socially acceptable among the university students (fit well with the students’ norm and environment). Another implication of this study is that Murdoch University (MU, Australia) students would most probably be willing to accept the adoption of a university smart card given that the results show MU students’ response of (probability=168/200) and NTU students’ response of 100% (probability=50/50) acceptance toward using their university smart card. The results also show a significant culture difference between MU students and NTU students, indicating that smart card manufacturer should take note of the ranking of user preference when designing a university smart card in order to optimize the adoption process.