Intelligent Multi-Agent Cooperative Learning System

Intelligent Multi-Agent Cooperative Learning System

Leen-Kiat Soh (University of Nebraska, USA) and Hong Jiang (University of Nebraska, USA)
Copyright: © 2006 |Pages: 7
DOI: 10.4018/978-1-59140-562-7.ch054
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Abstract

A computer-aided education environment not only extends education opportunities beyond the traditional classroom, but it also provides opportunities for intelligent interface based on agent-based technologies to better support teaching and learning within traditional classrooms. Advances in information technology, such as the Internet and multimedia technology, have dramatically enhanced the way that information and knowledge are represented and delivered to students. The application of agent-based technologies to education can be grouped into two primary categories, both of which are highly interactive interfaces: (1) intelligent tutoring systems (ITS) and (2) interactive learning environments (ILE) (McArthur, Lewis, & Bishay, 1993). Current research in this area has looked at the integration of agent technology into education systems. However, most agent-based education systems under utilize intelligent features of agents such as reactivity, pro-activeness, social ability (Wooldridge & Jennings, 1995) and machine learning capabilities. Moreover, most current agent-based education systems are simply a group of non-collaborative (i.e., non-interacting) individual agents. Finally, most of these systems do not peruse the multi-agent intelligence to enhance the quality of service in terms of content provided by the interfaces. A multi-agent system is a group of agents where agents interact and cooperate to accomplish a task, thereby satisfying goals of the system design (Weiss, 1999). A group of agents that do not interact and do not peruse the information obtained from such interactions to help them make better decisions is simply a group of independent agents, not a multi-agent system. To illustrate this point, consider an ITS that has been interacting with a particular group of students and has been collecting data about these students. Next, consider another ITS which is invoked to deal with a similar group of students. If the second ITS could interact with the first ITS to obtain its data, then the second ITS would be able to handle its students more effectively, and together the two agents would comprise a multi-agent system. Most ITS or ILE systems in the literature do not utilize the power of a multi-agent system. The Intelligent Multi-agent Infrastructure for Distributed Systems in Education (I-MINDS) is an exception. It is comprised of a multi-agent system (MAS) infrastructure that supports different high-performance distributed applications on heterogeneous systems to create a computer-aided, collaborative learning and teaching environment. In our current I-MINDS system, there are two types of agents: teacher agents and student agents. A teacher agent generally helps the instructor manage the real-time classroom. In I-MINDS, the teacher agent is unique in that it provides an automated ranking of questions from the students. This innovation presents ranked questions to the classroom instructor and keeps track of a profile of each class participant reflecting how they respond to the class lectures. A student agent supports a class participant’s real-time classroom experience. In I-MINDS, student agents innovatively support the buddy group formation. A class participant’s buddy group is his or her support group. The buddy group is a group of actual students that every student has access to during real-time classroom activities and with which they may discuss problems. Each of these agents has its interface which, on one hand, interacts with the user and, on the other hand, receives information from other agents and presents those to the user in a timely fashion. In the following, we first present some background on the design choice of I-MINDS. Second, we describe the design and implementation of I-MINDS in greater detail, illustrating with concrete examples. We finalize with a discussion of future trends and some conclusions drawn from the current design.

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