Accessing database information using mobile agents

Many real-world applications on the Internet require accessing database information and typical technology employed is client/server plus Web (C/S + Web). Although past few years saw many success applications by using this technique, there are still some drawbacks that need to be overcome. One of the drawbacks is that the transaction often fails if the network connection is unstable. Another disadvantage is high bandwidth requirement and latency. This paper argues that mobile agent technology provide an easy way to overcome the shortcomings in CIS + Web in database access on the Internet. A success case study using mobile agents to admit new students to China's institutions is then presented. The mobile agent was created by using IBM's Aglets Software Development Kit (SDK). Based on the experimental results, it is evident that mobile agent technology is well suited for such applications.

Virtual experts : who's online?

There will be between 30 and 80 million online students in the world by 2025. Globally, school systems are investing huge resources in the development of online education programs in order to meet this demand. Related implications for online teaching have been largely ignored despite evidence of the greatly increased workload for online teachers and widespread student dissatisfaction with online leaming experiences. Opportunities for improving the online experience for both leamers and teachers revolve around minimizing procedural inefficiencies in dealing with large numbers of individual students, as opposed to a single class, and of enhancing students' social and cognitive engagement with leaming. Intelligent software agents that can automate many routine online tasks and some aspects of leamer interaction have enormous potential to facilitate this. These agents that can act as a personal online coach, mentor or tutor to increase the individualisation of learning. The development of evidence-based agent personas is essential if agents are to fulfil specific educational roles. CUITently there is little progress being made in this area because of the lack of an agentrole model that can be used to implement specific educational personas in agents. In this paper we discuss key foundational elements of the nature and basis for implementing elements of educational expertise in software and how this could be used in developing agent persona models for specific educational roles and a model for implementing pedagogical constructs in intelligent educational software.

A virtual salesperson

This chapter describes the use of a graphical humane interface - a Virtual Salesperson. The face of the Virtual Salesperson is a generic Facial Animation Engine developed at the University of Genova in Italy and uses a 3-D computer graphics model based on the MPEG-4 standard supplemented by Cyberware scans for facial detail. The appearance of the head may be modified by Facial Definition Parameters to more accurately model the required visage allowing one model to represent many different Talking Heads. The “brain” of the Virtual Salesperson, developed at Curtin University, integrates natural language parsing, text to speech synthesis, and artificial intelligence systems to produce a “bot” capable of helping a user through a question/answer sales enquiry. The Virtual Salesperson is a specific example of a generic Human Computer Interface - a Talking Head.

Efficient algorithm for mobile multicast using anycast group

The authors present a novel and efficient multicast algorithm that aims to reduce delay and communication cost for the registration between mobile nodes and mobility agents and solicitation for foreign agent services based on the mobile IP. The protocol applies anycast group technology to support multicast transmissions for both mobile nodes and home/foreign agents. Mobile hosts use anycast tunnelling to connect to the nearest available home/foreign agent where an agent is able to forward the multicast messages by selecting an anycast route to a multicast router so as to reduce the end-to-end delay. The performance analysis and experiments demonstrated that the proposed algorithm is able to enhance the performance over existing remote subscription and bidirectional tunnelling approaches regardless of the locations of mobile nodes/hosts

Learning to dance with a human

Artificial neural networks are an effective means of allowing software agents to learn about and filter aspects of their domain. In this paper we explore the use of artificial neural networks in the context of dance performance. The software agent’s neural network is presented with movement in the form of motion capture streams, both pre-recorded and live. Learning can be viewed as analogous to rehearsal, recognition and response to performance. The interrelationship between the software agent and dancer throughout the process is considered as a potential means of allowing the agent to function beyond its limited self-contained capability.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Emergent behaviour: learning from an artificially intelligent performing software agent

This research investigates the possibility for emergent choreographic behaviour to arise from the interactions between a human dancer and a learning, digital performing agent. The cognitive framework is extended through theories of distributed cognition to take into account the two interacting agents rather than a single agent and its environment. The Artificial Neural Network based performing agent demonstrated emergent dance behaviour when performing live with the human dancer. The agent was able to follow the dancer, create movement phrases based on what the dancer was performing and recognize short movement phrases, as a result of the interaction of the dancer’s motion captured movement data and the agent’s artificial neural network. This emergent behaviour was not explicitly programmed, but emerged as a result of the learning process and the interactions with the human dancer.