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Designing Distributed Learning Environments with Intelligent Software Agents Fuhua Oscar Lin Athabasca University, Canada Acquisition Editor: Mehdi Khosrow-Pour Senior Managing Editor: Jan Travers Managing Editor: Amanda Appicello Development Editor: Michele RossiCopy Editor: Lori Eby Typesetter: Amanda Appicello Cover Design: Lisa TosheffPrinted at: Yurchak Printing Inc. Published in the United States of America by Information Science Publishing (an imprint of Idea Group Inc.) 701 E. Chocolate Avenue, Suite 200Hershey PA 17033Tel: 717-533-8845Fax: 717-533-8661E-mail: cust@idea-group.comWeb site: http://www.idea-group.com and in the United Kingdom by Information Science Publishing (an imprint of Idea Group Inc.) 3 Henrietta StreetCovent GardenLondon WC2E 8LUTel: 44 20 7240 0856Fax: 44 20 7379 3313Web site: http://www.eurospan.co.uk Copyright © 2005 by Idea Group Inc. All rights reserved. No part of this book may be reproduced in any form or by any means, electronic or mechanical, inclu Organization of This Book Chapter 1: A Human Collaborative Online Learning Environment Using Intelligent Agents Hilton José Silva de AzevedoParaná Federal Center for Technological Education, Brazil Edson Emílio ScalabrinPontifical Catholic University of Paraná, Brazil Abstract This chapter introduces the design and implementation of a multiagent system based on a collaborative online learning environment (COLE). The purpose of developing such an environment is to improve social competences along with traditional content-related ones in lifelong learning. As educators would be unable to handle the huge amount of data concerning human interactions in such a learning environment, a multiagent system approach is adopted. The concept of human collaboration and the ways that project-based learning (PBL) and portfolios can be used to improve social competences are discussed based on the Social Theory of Learning. The way that the System Analysis for Agent Systems (SAAS) method was used to identify services and agents Chapter 1: A Human Collaborative Online Learning Environment Using Intelligent Agents Introduction The recent increase in information production and the consequent necessity to process it have been justifying research on the use of multiagent systems. The control of power or chemical plants, data searching on the Web, and the study of artificial life are examples of activities that can benefit from the use of these systems. The use of intelligent agents is a useful approach, because they can process huge amounts of data (from sensors or databases), intervene directly in processes, and interact with other agents to achieve their tasks. The aim of this chapter is to discuss the role of agents not only in the enhancement of existing processes but also as a framework with which to design new processes. The chapter focuses on using agents to implement a learning environment that enables its human users to develop social competences rather than just technical ones. Most learning management systems (LMSs) for either face-to-face or distance education take into account only pun A Human Collaborative Online Learning Environment Human Collaboration In this present study, “human collaboration in a group” means a set of intentional actions that one makes in order to help another member of a group accomplish a task or an activity that is relevant to the group. We argue that the existence of interactive tools such as e-mail, discussion lists, forums or chat sites is not enough to configure cooperative environments, even for working or learning. This assumption is based on the present approaches of Learning Social Theory (Engeström, 1999; Wenger, 1998). Such a theoretical model is based on the assumption that human beings continuously need to construct their identities to motivate them to participate in social activities. In such a context, every action is meaningful in terms of how people recognize themselves and are recognized by others. According to this approach, human activities in which cooperation can be identified are those that have something more than a co Identification of Services and Agents Nowadays, one can easily find in the Internet commercial and academic environments in which agents can be implemented, but finding methodologies to design multiagent systems is not easy. Some ideas may come from software engineering, like that for Object Design (Coad & Yurdon, 1991) and OMT and UML (Blaha & Premerlani, 1997); knowledge engineering like KADS (Wielinga & Schereiber, 1990), KOD (Vogel, 1988), MKM (Ermine et al., 1996), and REX (Malvache & Prieur, 1993), or from enterprise modeling (Fox, 1998; Wiig, 1993, 1994) if the services are well defined. In our case, the services we needed did not yet exist in practice, and every system specification had to be based on what was known from the limited use of portfolios, PBL, and from the past efforts to implement some of the concepts in the theoretical framework. For this reason, we used the SAAS method to help in the process of analysis and agent specification in the context of human activities Multiagent System Architecture A COLE is a complex system in which human learning activity continuously generates new patterns of behavior and knowledge. In a unique intelligent system, assembling all the necessary knowledge (expertise) needed to perform complex tasks normally done by humans is difficult. Distributed architectures seem to be most appropriate for handling the intelligence in complex systems. The agents of a COLE must be based on the dynamic environment and learn with it in order to help its users. This requirement suggests the use of a distributed open architecture that will allow the system to adapt itself to different contexts by adding new services. Multiagent systems have a distributed nature that allows for local reasoning and dynamic integration of new agents; they are able to evolve with the kind of complex systems that were described. Multiagent systems can be classified according to their architectures (overall organization), the degree of autonomy of each agen Architecture of the COLE Several issues must be considered when designing and implementing online learning environments. Larry et al. (2004) conducted a necessary and interesting discussion about security in such environments. Within the COLE, the focus of the work is on how intelligent agents can help build an effective collaborative online learning environment. The COLE has an open architecture. The concept of an open architecture is used in several areas in computer science: (a) in the interconnections between the architectures of different machines (the OSI model), (b) in the dynamic binding between a client and a server (the CORBA model), (c) in dynamic reconfiguration and mutual selection (the Contract-Net model), and (d) in the dynamic integration of an agent in a work context (the agent model). The dynamic integration of an agent implies its capability to learn about the current tasks going on. In most multiagent systems, whenever a change occurs, everything must be brought to Discussion The choice of intelligent agents is based on online students’ need for good support in a PBL context. The role these agents have to play is new, and they have to deal with three simultaneous dimensions: The technical (how to work with portfolios and use the environment’s available tools) The pedagogical (how to construct their own representation about a given domain and use a graphic environment to represent it) The strategic (how to use and develop their own social competences to achieve their goals in the collaborative PBL context) Educators responsible for following up details from all three dimensions would not be able to pay sufficient attention to the aim of the learning process. Intelligent agents can monitor students’ steps and, according to the knowledge models they have, inform students about procedures the students are not yet used to. The following examples illustrate how the agents can be intelligent: In order to have an idea in the group portfolio, a student ha References Azevedo, H. de (1997). Contribution à la modélisation des connaissances à l’aide des systèmes multi-agents. Ph.D. Thesis, University of Technology of Compiègne, France, 169 (in French). Barthès, J. -P. A., & Azevedo, H. J. S. (1998). Identifying autonomous agents for capitalizing knowledge in R&D environments. Electronic Edition (CEUR Workshop Proceedings). Beer, R. D. (1992). A dynamical systems perspective on autonomous agents. Special Issue, AI J. on Computational Theories of Interaction & Agency. Blaha, M., & Premerlani, W. (1997). Object-oriented modeling and design for database applications. New York: Prentice-Hall. Bond, H., & Gasser, L. (1988). What is DAI ? Reading in distributed artificial intelligence. New York: Morgan Kaufman Publishers. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, (47), 139–159. Cammarata, S., McArthur, D., & Steeb, R. (1983). Strategies of cooperation in distributed problem solving. Karlsruhe: IJCAI’93, (2 Chapter 2: Intelligent Agents Supporting Distributed Collaborative Learning Weiqin ChenUniversity of Bergen, Norway Barbara WassonUniversity of Bergen, Norway Abstract In the context of distributed collaborative learning, it is usually difficult for students to be aware of others’ activities and for instructors to overview the process and regulate the collaboration. In order to facilitate collaborative learning, intelligent agents were developed to support the awareness and regulation of the collaboration. This chapter discusses the facilitation role of intelligent agents and how they support students and instructors in distributed collaborative-learning environments. By monitoring the collaboration, the agents compute statistics, detect possible problems, and give advice synchronously and asynchronously to the students and instructor based on their activities and requests. In so doing, the agents not only help students to self-regulate their activities but also help instructors to mai Chapter 2: Intelligent Agents Supporting Distributed Collaborative Learning Introduction Agent technology has been used in educational environments for some time, and a number of agents and multiagent systems have been designed specifically for educational purposes. In these systems, agents play different roles, such as tutors (Johnson et al., 2000) or co-learners (Chan, 1996). Another role for an agent is that of a facilitator (Chen & Wasson, 2003). For example, in a distributed collaborative-learning environment where users are geographically distributed and collaborate through a Web-based learning environment, an agent can facilitate collaboration processes such as coordination, teacher intervention, group interaction, etc. In computer-supported collaborative work (CSCW), facilitation was studied in group supporting systems (GSSs) (Hirokawa & Gouran, 1989; Pollard & Vogel, 1991; Antunes & Ho, 1999). The activities of the facilitator in supporting group work have been identified. They are, among others, ensuring member identity and maintaining a discussion f Design Issues of Intelligent Agents as Facilitators In this section, we discuss design issues of facilitator agents in distributed collaborative-learning environments, including problems that often occur in the collaboration process, awareness, and how to present the awareness information and advice effectively and nonintrusively. Software Agents and Pedagogical Agents The term “agents” has been used in a variety of fields of computer science and artificial intelligence. It has been applied in many different ways in different situations for different purposes. However, there is no commonly accepted notion of what it is that constitutes an agent. As Shoham (1993) pointed out, the number of diverse uses of the term “agent” are so many that it is almost meaningless without reference to a particular concept of agent. Many researchers have attempted to address this problem by characterizing agents along certain dimensions. For example, Franklin and Graesser (1997) constructed an agent taxon Facilitator Agents in FLE This section describes the facilitator agent we have built for an FLE learning environment—its design, implementation, and evaluation. FLE FLE (Muukkonen et al., 1999) is a Web-based groupware for computersupported collaborative learning (CSCL). It is designed to support a collaborative process of progressive inquiry learning. Progressive inquiry (Figure 2) entails that new knowledge is not simply assimilated but jointly constructed through solving problems and building mutual understanding (Scardamalia & Bereiter, 1996). The main ideas behind this model are the development of self- regulative and metacognitive skills (Boekaerts, 1999), reflective and criticalthinking skills (Beyer, 1985), and demonstrated academic literacy in reading and writing (Geisler, 1994). Self-regulated learners are generally characterized as active learners who efficiently manage their own learning in different ways. Self-regulated learning is an active construction process whereby le Facilitator Agent in Mindmap Building Tool In this section, the design and implementation of the facilitator agent in Mindmap Building Tool will be described. Mindmap Building Tool The Mindmap program is a collaborative tool where distributed users model their conceptual understanding of a topic. The workspace consists of an individual and a shared Mindmap. Users can switch between these two maps by pressing the “teleview” buttons. Based upon each individual contribution in their personal Mindmaps, the group members must negotiate and agree upon one common representation (Buzan, 2000). The main purpose of this program is for users to have a meeting place where they can brainstorm and build individual Mindmaps and joint Mindmaps. The agent has two roles in this program. The first is to monitor user actions. This includes saving data into log files, updating the internal representation of the environment. The second role of the agent is to function as a coordinator, meaning it will not c Related Work Concerning agents in facilitating CSCL, several related works are worth noting. Understanding Collaboration In order to provide efficient and effective support to students in distributed collaborative-learning environments, many researchers choose to start by understanding the collaboration. Therefore, most efforts in facilitator agents such as IDLC (Okamoto et al., 1995), GRACILE (Ayala & Yano, 1996), and EPSILON (Soller, 2001) have been placed on designing intelligent modules that replace the instructor’s role in the collaboration. In order to obtain this goal, students are restricted to using “semistructured” interfaces such as menu-driven or sentence-openers to collaborate, which restrain the interaction channels and slow the communication process. Furthermore, the advice generated by these intelligent systems is based on its own understanding of the collaboration process, which has a high possibility of misinterpretation or misunderstanding. As a result, the advice mi Conclusions and Future Work This chapter presents intelligent agents facilitating distributed collaborative learning. It covers agent design issues and implementation details. In our research, we provide different support to users (including students and instructors). We have combined awareness information and advice, agent regulation, students’ self-regulation, and instructor regulation. The performances of these agents have been explored in various scenarios, both in asynchronous and synchronous collaborative environments, and positive feedback was received from students and instructors. Several issues merit our further investigation: Determining what feedback is useful to the students is an empirical question. A fine-tuning of content of messages is a collaboration between the instructors and the system developers, taking into consideration the student’s opinion about the usefulness of the feedback. This is one of the issues in focus during the current user testing. In our design, a Acknowledgments This project is a part of DoCTA-NSS, a project funded by ITU (IT in Education) program of KUF (Norwegian Ministry of Church Affairs, Education, and Research). The authors would also like to thank the anonymous reviewers for their constructive comments and all the participants, especially Jon Dolonen, Steinar Dragsnes, Rune Baggetun and Anders Mørch, in the pedagogical agent group in DoCTA-NSS. References Alarcon, R., & Fuller, D. (2002). Intelligent awareness in support of collaborative virtual work groups. Paper presented at the CRIWG2002. Antunes, P., & Ho, T. (1999). Facilitation tool—A tool to assist facilitators managing group decision support systems. In Proceedings of the Ninth Workshop on Information Technologies and Systems (WITS ’99). Charlotte, North Carolina. Ayala, G., & Yano, Y. (1996). Intelligent agents to support the effective collaboration in a CSCL environment. Paper presented at the Ed- Telecom’96, Charlotteville, VA. Baggetun, R., Dolonen, J., & Dragsnes, S. (2001). Designing pedagogical agent for collaborative telelearning scenarios. Paper presented at the 24th IRIS, Ulvik, Norway. Barros, B., & Verdejo, M. F. (1999). An approach to analyse collaboration when shared structured workspace are used for carrying out group learning processes. Paper presented at the AIED’99, Lemans, France. Barros, B., & Verdejo, M. F. (2000). Analysing student interaction pr Chapter 3: Privacy and Trust in Agent-Supported Distributed Learning[1] Larry KorbaNational Research Council Canada, Canada Yuefei XuNational Research Council Canada, Canada Andrew S. PatrickNational Research Council Canada, Canada George YeeNational Research Council Canada, Canada Ronggong SongNational Research Council Canada, Canada Khalil El-KhatibNational Research Council Canada, Canada Abstract The objective of this chapter is to explore the challenges, issues, and solutions associated with satisfying requirements for privacy and trust in agent-supported distributed learning (ADL). Accordingly, the first section will present the background, context, and challenges. The second section will delve into the requirements for privacy and trust as seen in legislation and standards. The third section will look at available technologies for satisfying these requirements. The fourth section will discuss an often- ignored area—that of building trustworthy user interfaces for distributedlearn Chapter 3: Privacy and Trust in Agent-Supported Distributed Learning[1] Introduction Background and Context One of the key characteristics of our information economy is the requirement for lifelong learning. Industrial and occupational changes, global competition, and the explosion of information technologies have highlighted the need for skills, knowledge, and training. Focused on attracting and retaining staff, companies have placed an emphasis on training to bolster soft and hard skills to meet new corporate challenges. In many cases, career training has been placed in the hands of employees, with the understanding that employees must be able to keep ahead of technological change and perform innovative problem solving. One way of meeting the demand for these new skills (especially in information technology) is through online distance learning, which also offers the potential for continuous learning. Moreover, distance learning provides answers for the rising costs of tuition, the shortage of qualified training staff, the high cost of campus maintenance, Privacy Legislation and Standards Privacy Legislation Jurisdictions in countries throughout North America and Europe have realized the need to protect consumer privacy and have enacted privacy legislation for this purpose. In these countries, where there is privacy legislation, individual control is required for the use of personal information, including the collection, use, disclosure, retention, and disposal of personal data by organizations that may handle that information. Privacy principles have been developed to expose the implications of privacy laws or privacy policy adopted by online organizations. In Canada, 10 Privacy Principles (CSA 1) (see Table 2), incorporated in the Personal Information Protection and Electronic Documents Act of Canada (Department of Justice), spell out the requirements for use of personal information. These principles may be implemented in computer systems to varying degrees due to the nature of each principle or the underlying application. For example Privacy-Enhancing Technologies for ADL Policy-Based Privacy and Trust Management Policy-based management approaches have been used effectively to manage and control large distributed systems. In such a system, policies are usually expressed in terms of authorization, obligation, delegation, or refrain imperatives over subject, object, and actions. These policies are expressed using a policy specification language, such as Ponder or XACL, introduced in the next section. While policies expressed using Ponder or XACL can be compiled and enforced in the system, other policy languages can be used simply to inform the user about the practices adopted by the system. These policies depend on other mechanisms for implementation and enforcement. An example of such a policy language is the Platform for Privacy Preferences Project (P3P) (P3P: The Platform for Privacy Preferences Project, 2001), developed by the World Wide Web Consortium (W3C). Subsection “P3P” elaborates more on the P3P and its us Promoting Trust in ADL Systems In this section, we focus on how to persuade the individual learner to trust and accept ADL systems and thereby be comfortable using them. We begin by discussing the impact of privacy, security, and trust in the learning process. We follow this by presenting an important component that engenders trust in ADL—the design of trustable user interfaces. The Impact of Privacy, Security, and Trust on the Learning Process In the above sections, privacy and security were discussed in terms of legislation, standards, and technology. Yet, the greatest challenge in the adoption of ADL may be learner acceptance of ADL technology. Holt et al. (2001) reported a number of obstacles to e-learning, including learner anxiety and resistance to computers brought about by the concerns for the privacy and security of a learner’s data. They indicate that this may potentially lead to other negative implications, including alienation, inadequacy, loss of responsibility, and damage Conclusions and Further Research Within this chapter, we have described many aspects associated with building privacy into agent-supported distributed learning. We describe how the LTSA model may be applied to ADL environments. Probing deeper into privacy requirements, we started with the privacy principles to interpret the technologies that may be applied to ADL to provide privacy. Trust and policy systems, policy negotiation, ways of reducing learner anxiety, trustable human–computer interfaces, secure distributed logging, anonymity systems, and network confidentiality approaches can all play a role in meeting privacy requirements in ADL implementations. We also present a privacy architecture for agent- based e-commerce that may be applied to ADL. However, it is clear that not all of these technologies would be required for every learning environment. For instance, an agent-based learning environment designed for delivering most undergraduate university or community college courses w References Advanced Distributed Learning. Retrieved March 3, 2003 from the World Wide Web: http://www.adlnet.org Alliance of Remote Instructional Authoring and Distribution Networks for Europe. Retrieved March 3, 2003 from the World Wide Web: http:/ /www.ariadne-eu.org Aviation Industry CBT Committee. Retrieved March 3, 2002 from the World Wide Web: http://aicc.org Back, A., Goldberg, I., & Shostack, A. (2001, May). Freedom 2.1. Security issues and analysis. Bellotti, V., & Sellen, A. (1993). Designing for privacy in ubiquitous computing environments. In Proceedings of European Conference on Computer- Supported Cooperative Work, ECSCW ‘93. Milan, Italy. Blaze, M., Feigenbaum, J., & Lacy, J. (1996). Decentralized trust management. In Proceedings of the 17th IEEE Symposium on Security and Privacy (pp. 164–173), IEEE Computer Society. Blaze, M., Feigenbaum, J., Ioannidis, J., & Keromytis, A. D. (1999). The KeyNote Trust-Management System Version 2, Request For Comments (RFC) 2704. Retriev Chapter 4: Intelligence in MAS-Based Distributed Learning Environments Chunsheng YangNational Research Council of Canada, Canada Abstract This chapter first addresses the issue of the importance of intelligence in MAS-based DLEs. Then, it stresses that there are three main intelligent competencies in MAS-based DLEs: intelligent decision-making support, coordination and collaboration of the agents in MAS, and student modeling for personalization and adaptation in learning systems. It also describes in detail how to apply relevant AI techniques, including the introduction of AI techniques and their state-of-the-art application in the e-learning domain. Finally, future trends in the research and development of intelligence for MAS-based DLEs are discussed. Chapter 4: Intelligence in MAS-Based Distributed Learning Environments Introduction With the rapid development of broadband-based communication networks such as wireless networks and optical networks, the multiple agent system (MAS), an effective and feasible infrastructure for distributed-learning environments (DLEs), has been widely used to facilitate advanced leaning environments and solve many current problems of existing education systems. Over the past few years, many universities and colleges have made substantial progress in using MAS-based distributed-learning systems such as Web-based learning tools and systems, designed for teaching and learning, and for distance-learning applications. Today, it is desirable for MAS-based distributed-learning environments to provide more smart or intelligent learning functions that offer personalized services with capabilities to learn, reason, have autonomy, and be totally dynamic (Jafari, 2002). With intelligent learning environments, students can study their chosen subjects at any time and from anywhere, and Why Intelligence? First, what is the intelligence in MAS-based DLEs? Intelligence is a sort of competence for the agents to perform specified tasks while acting as human beings. The intelligence is an embedded software component that integrates software technology and complicated algorithms. The algorithms will “guide” software to perform specific tasks for human beings. Why is such intelligence necessary and important in MAS-based DLEs? To answer this question, let us start from a learning and teaching scenario in MAS-based DLEs: Peter Orchard, a student at University, would like to take a course in Political Science, and he logs onto the portal at the University. Once he logs on, an agent [called interface agent (Lin et al., 2003) in MAS] will look at his profile, his background knowledge, and course historic records to specify a model for him and select a subject, “Politics 101” that fits his interest and background knowledge very well. So, Peter undertakes this course using learnin Case-Based Reasoning for Distributed-Learning Environments CBR Overview Case-based reasoning is one of the major reasoning paradigms in artificial intelligence. A CBR reasoner[1] solves new problems by retrieving a similar problem from a case base, which stored the experienced solutions to past problems. When the reasoner cannot find a solution that is similar enough to solve the new problem, CBR will adapt the solution of a relatively similar problem to the new one. In principle, CBR is different from other AI reasoning approaches such as rule-based reasoning (Yang et al., 2003) and model-based reasoning. In the case of the rule-based reasoning system, for example, expert systems, the rules reflect the certain relationships between the problems and their solutions. The rules can be designed from the text-based documents or from domain expert’s experience and know-how. However, the cases in CBR systems reflect or document the relation between the problems and their solutions, which wer Symbolic Machine Learning for Student Modeling Machine learning has been widely used in intelligent software agents as an effective and feasible approach to improving an agent’s intelligent ability in MAS. Most research focuses on developing the personalized and adaptive mechanism for agents to manage the information available on the Web site. Specifically, lots of efforts focus on the adaptive mechanism of Web-based education systems (Guven et al., 1998a, 1998b, 2000; May et al., 2001). Guven and Blandford (1998a) successfully developed the Mltutor, which used a symbolic multialgorithmic learning method to analyze users’ navigational patterns within Web-based educational hypertext systems, in order to provide adaptation in the form of recommendations considered most relevant to the learner’s current area of learning. Mayo and Mitrovic (2001) proposed to optimize the behavior of ITSs (intelligent tutoring systems) using Bayesian networks and decision theory (Briscoe & Gaelli, 1996). Ma Knowledge-Based Intelligent Support in MAS-Based DLEs Rule-based reasoning or a knowledge-based system is one of the most successful research topics in AI. Knowledge-based systems have been widely applied to different domains, such as medicine, diagnosis, complex system maintenance, decision-making support, and so on. Undoubtedly, knowledge- based decision-making support is also an important alternative to provide intelligent support for DLEs. It is preferred that DLEs not only be used for distance learning but also for training in complicated and high-cost applications. For example, to deal with the extremely high cost of navigator training in a real navigation environment, applying MAS and AI technologies to design effective DLEs in realistic simulating environments will be highly cost effective. In this section, we discuss how to apply rule-based reasoning techniques to DLEs by introducing an intelligent learning environment for training navigators. Background The recent devastating Future Trends A great deal of the accomplishments in AI research, along with wide applications to MAS and e-learning, are fueling development of the intelligent support for MAS-based DLEs by applying AI techniques. However, to design more useful, more advanced, and more widely applicable MAS-based DLEs, we still have many challenges, such as the proper handling of the huge amount of data, the reduction of noise and error in data, the negotiation between agents in different educational institutes, the management and explanation of knowledge for DLEs, and so forth. Fortunately, these issues can be solved by using the latest advanced AI techniques, including data mining, fuzzy CBR, policy-based approach, semantic Web, etc. There are some positive trends in the development of intelligence for MAS-based DLEs. The main trends are as follows: Applying data mining to student modeling to improve the model accuracy Using integrated reasoning techniques to build more advanced intelligent support Conclusions In this chapter, we first addressed the issue of the important intelligence in MAS-based DLEs. We then emphasized three main intelligent competencies in MAS-based DLEs: intelligent decision-making support, coordination and collaboration of the agents in MAS, and student modeling for personalization and adaptation in learning systems. We also described in detail the application of relevant AI techniques, including the introduction of AI techniques, and their state-of-the-art applications in the e-learning domain. For intelligent decision-making support, a knowledge-based system, that is, a rule-based reasoning technique, is suitable and feasible. The example presented, an intelligent learning environment for training navigators, distinctly exhibited the application of the rule-based reasoning technique to MAS-based DLEs for implementing intelligent support. This approach is also useful for other training applications, such as aviation training, firefighter training, and so o References Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations and system approaches. AI Communications, 7(1), 39–59. Aha, D. W., & Maney, T. (1997). A model-based approach for supporting dialogue inferencing in a conversational case-based reasoner. Presented at the AAAI-98 Spring Symposium on Multi-modal Reasoning. (NCARAI Technical Report AIC-97-023). Aha, D. W., Bereslow, L. A., & Avila, H. M. (1999). Conversational case- based reasoning. Applied Intelligence, 1–25. Aha, D. W., Breslow, L. A., & Maney, T. (1998). Supporting conversational case-based reasoning in an integrated reasoning framework. The 1998 workshop of case-based reasoning integrations. Arai, S., & Sycara, K. (2000). Effective learning approach for planning and scheduling in multi-agent domain. In Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior (From animals to animates) (pp. 507–516). Baffes, P., & Mooney, R. (1996). Refinement-base Chapter 5: Knowledge Management for Agent-Based Tutoring Systems Ping ChenUniversity of Houston-Downtown, USA Wei DingUniversity of Houston-Clear Lake, USA Abstract As the education field is becoming increasingly technology heavy, more educational systems involve line or interactive training and tutoring techniques, and lots of educational information becomes available via the intranet and World Wide Web. Managing large volumes of learning information and knowledge is one of the crucial issues for these educational systems, as appropriate knowledge management is the key to more effective and efficient learning. The chapter first discusses that an intelligent agent system could be successfully applied to the education field and then focuses on how knowledge management techniques play an important role in agent-based tutoring systems. Chapter 5: Knowledge Management for Agent-Based Tutoring Systems Introduction Computer technologies are making progress rapidly and are becoming more specialized. Many different fields have benefited from newly invented and powerful computer technologies. Therefore, it is not a surprise that education adopts more computer technologies, and students and learners use computers in a lot of courses and labs. New technology integrated into the education or tutoring system can enhance the access to knowledge and improve the efficiency of knowledge transferring to learners. But such integration often requires additional training in order for its users to become familiar with a new learning environment before they can actually benefit from these technology advances; otherwise, new technology will confuse and distract, instead of helping, its users, and slow the learning process. Agent-based tutoring systems can overcome such technical obstacles between knowledge and common users. Then, users are able to focus on information and knowledge that they are inter Background Learning is an active, interactive, and constructive social process. Technology, especially computer technology, can help learning greatly. Initially, the learning technology focused on individualized instruction, i.e., stand-alone tutoring, a universal environment for all students. The current view of training and education environments must support customized inquiry-based learning and collaboration, and such an environment has the following advantages over the old learning technologies: Intelligent tutoring systems have explicit tutoring models and domain knowledge that can serve each individual in a more customized and efficient way. Interactive learning systems enable the student to manipulate cognitive artifacts from several perspectives or viewpoints (Norman, 1992). Cooperative learning systems provide students with access to other people’s ideas and concepts, and this makes it possible to exchange, discuss, negotiate, defend, and synthesize viewpoints (SIGCUE, 1992). Multi-Agent-Based Educational Environment Wooldridge and Jennings (Wooldridge, 1994) gave one of the most comprehensive definitions of agents: …a hardware or (more usually) a software-based computer system that enjoys the following properties: autonomy—agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; social ability—agents interact with other agents (and possibly humans) via some kind of agent-communication language; reactivity— agents perceive their environment and respond in a timely fashion to changes that occur in it; pro-activeness—agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking initiative. We call agents used in an education environment the “educational agents.” The role of the educational agent is to provide task-related feedback and assistance to the learner and to guide the learner through the learning process and help the Knowledge Management for a Tutoring System Knowledge is a principal factor that makes personal, organizational, and societal intelligent behavior possible (Wiig, 1995). Knowledge management consists of activities focused on the organization gaining knowledge from its own experience and from the experience of others, and on the judicious application of that knowledge to fulfill the mission of the organization (Wiig, 1995). In the context of a learning environment, such an organization consists of a group of students. These activities are executed by integration of technology, organizational structures, and cognitive-based strategies to convey existing knowledge and produce new knowledge. The critical step is the enhancement of the cognitive system in acquiring, storing, and utilizing knowledge for learning, problem solving, and decision making. Knowledge management is stated as the management of the organization (an individual student or a group of students in our context of learning en Future Trends We believe that agent-based tutoring systems will provide a means of dealing with the knowledge acquisition, revision, and transfer that are essential in a learning process. Agents will use a variety of communication and representation modes to help us to understand and make use of course materials or knowledge. We are sure that learning environments employing multiagents systems allow students, teachers, and courseware developers to add flexibility in achieving their learning objectives. To make such a system more helpful, future study may concern the following problems: Different architecture design for different courses, topics, or disciplines Because different courses, topics, or disciplines involve knowledge that is very different in presentation or nature, such as history and mathematics, we may need different knowledge management architectures and techniques for them. How will Internet technology affect an agent-based tutoring system? Modern tutoring systems should Further Reading Some researchers (Mizoguchi et al., 1988; Kono et al., 1992; Giangrandi & Tasso, 1995) applied truth (or reason) maintenance systems (TMSs) (Doyle, 1979; DeKleer, 1986) to overcome conflicts between new and old knowledge. The TMS identifies the conflicts, and some domain-specific reasoning system will resolve them. Huang and McCalla (1992) and Huang (1994) developed “Logic of Attention,” a variant of the TMS that focuses on the parts of the student model and instructional planner that are relevant to the current subgoals. Bruff and Williams (2000) proposed an architecture in which the problem of conflicting information is resolved using methods based on the AGM paradigm for belief revision (Alchourron et al., 1985). Bruff and Williams used possibility theory (Dubois, 1992) to address the problems of uncertain information, nonmonotonic reasoning, and default logic (Reiter, 1980); the formalism (Antoniou, 1996) to process incomplete information; and Theory Extraction for Conclusions In this chapter, we discussed how agent technology could be used in an education environment and how knowledge is managed in such a system. Although many problems remain to be solved, we believe that agents can model and manage knowledge in an appropriate way, and agent technology will be an important step to improve the effectiveness and efficiency of a tutoring system. References Alchourrón, C., Makinson, D., & Gärdenfors, P. (1985). On the logic of theory change: Partial meet functions for contraction and revision. Journal of Symbolic Logic, 50, 510–530. Antoniou, G., & Williams, M.-A. (1996). Reasoning with incomplete and changing information. Journal of Information Science, 99(1 & 2), 83–99. Bertel, T. What is Knowledge Management? The Knowledge Management Forum. Available on the World Wide Web: http://www.km-forum.org/what_is.htm Bruff, C. M., & Williams, M. -A. (1999). An intelligent tutoring system architecture based on knowledge representation and reasoning. In Proceedings of the Seventh International Conference on Computers in Education (pp. 736–743). IOS Press. Bruff, C. M., & Williams, M. -A. (2000). An agent-based intelligent tutoring system. Eighth International Conference on Computers in Education (pp. 427–433). IOS Press. De Diana, I., & Aroyo, L. Knowledge management for networked learning environments: Applying intelligent agents. Ava Chapter 6: Intelligent Tutoring Systems for Distributed Learning Mohamed AllyAthabasca University, Canada Abstract This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development. Chapter 6: Intelligent Tutoring Systems for Distributed Learning Introduction According to a recent panel (Corbett, Anderson, Graesser, Koedinger, & VanLehn, 1999), the current generation of intelligent tutoring systems is only half as effective as human tutors, and we need to develop tutoring systems that are as effective as human tutors. We need to study human tutors when they provide one-to-one instruction in distributed-learning systems and use the expertise to build intelligent tutoring systems. Existing distributed-learning systems are designed to instruct students based on information already stored in memory. The systems do not adapt to the needs of the learner by diagnosing, in minute detail, the sources of errors and by providing specific instruction to overcome the errors. Distributed-learning systems need to form a model of the learner and provide instruction similar to a tutor in a one-to-one interaction mode. Bloom (1984) described the two-sigma problem, which suggests that learners who are given one-to-one instruction performed two st Background Before the details of intelligent tutoring systems are covered, it is important to discuss intelligent agents, because an intelligent tutoring system is considered to be an intelligent agent system. Wooldridge and Jennings (1995) defined an intelligent agent as a computer system that is capable of flexible autonomous action in order to meet its design objectives. The intelligent agent in an intelligent tutoring system performs on behalf of the tutor to help learners achieve learning outcomes and to prescribe teaching strategies based on learners’ profiles in the student model and content in the domain module. As the agent interacts with the learner, it gains more experience by learning about the learner. The expertise in the intelligent tutoring system intelligent agent should allow the agent to help learners achieve the learning outcome without human intervention. The intelligent agent should anticipate learners’ responses and respond immediately to take corrective action o The Student Intelligent tutoring systems are built for students, so the students should be the focus when developing any intelligent tutoring system. When students come to the learning process, they come with many individual differences, such as unique learning styles, different motivational levels, different backgrounds, different levels of expertise, and different expectations. The question is how to develop an intelligent tutoring system that identifies these individual differences and adapts the instruction to meet learners’ individual needs. A well-designed intelligent tutoring system will be able to cater to learners’ individual needs in a distributed environment. As researchers, we need to build tutoring systems to benefit students and help students learn. Students learn more effectively when they are active in the learning process. Active learning results in a deeper level of processing, which results in better storage in long-term memory (Craik & Lockhart, 1972). Active learni Domain Module The domain module consists of the domain knowledge and can fall in a continuum between an opaque or black-box representation or a transparent or glass-box representation. In the black-box representation, only final results are available to the learner, whereas in the transparent representation, each reasoning step can be inspected and interpreted by the learner (Wenger, 1987). In an intelligent tutoring system, the knowledge representation should be transparent to allow the learner to see the reasoning on how to solve problems (Wenger, 1987). This is critical for learners who are new in the field so that they can develop the appropriate line of reasoning. The domain module contains the domain knowledge that the system intends to teach. The domain knowledge includes both the content to be taught and the application of that knowledge to solve related problems. The application of the knowledge is critical for developing higher-level learning outcomes and for learners to pers Student Model With the advance in artificial intelligence techniques and the power of computers, modeling student behaviors has become possible for inclusion into intelligent tutoring systems. The modeling process allows the system to predict the learning behavior of individual students and diagnose the causes of errors (Dede, 1986). However, to do this, one needs a model of the learner, which represents cognitive processes (e.g., information processing, analysis, synthesis, information retrieval, calculation, and problem solving); metacognitive strategies (e.g., learning from errors, when to access more information, when learning outcome has been reached); and psychological attributes (e.g., developmental level, learning style, motivational level, and interests). The student model develops hypotheses about the student’s misconceptions and inferior performance strategies so that the pedagogical module can point them out, indicate why they are wrong, and suggest corrections (Tennyson & Pedagogical Module The pedagogical module consists of a set of specifications of what instructional materials the system should present and how and when it should present the materials. The pedagogical module should behave like an expert teacher who provides timely feedback, examples, analogies, multiple views, and different levels of explanations (Clancey, 1987). The system must monitor the student’s actions, advise the student about errors, and anticipate future actions based on inferences about the student’s current activities (Woolf, 1987). The system should know how to ask the right questions and how to focus on the appropriate issues. In intelligent tutoring systems, two methods are usually used when interacting with students: the Socratic and the coaching methods. In the Socratic method, students are provided with questions that guide them through the process of debugging their misconceptions. In the debugging process, students are assumed to reason about what they know and do n Interface Module An interface module is required to coordinate the interaction between the student and the components of the intelligent tutoring systems. The intelligence built into the interface module must learn from experience by analyzing what the learner did in the past and adapting the interface for future interaction with the system. The interface module works with the pedagogical module to learn about the students so that the appropriate interface attributes can be developed for the learner. The interface module must be proactive by anticipating what the learner will do next and providing the most appropriate interface for the interaction. The interface module should also be able to sense the motivational level of the learner and use interface techniques that motivate learners. For example, if the subject matter of the learning session is tedious and boring for the learner, the agent in the interface module must be able to predict this motivational state and adjust the interfa Future Trends The education and training field is changing dramatically, and businesses and organizations need systems to help them cope with the changes to become more effective. With the increasing use of networks and the Internet, more businesses, organizations, and institutions will be moving away from face-to-face group instruction to distributed learning. As a result, there is a sense of urgency to develop intelligent tutoring systems to provide effective training and education for learners and employees. Below are some trends in distributed learning that will have an impact on the design and development of an intelligent tutoring system: Rather than present entire courses to learners, the content will be developed into different levels of granularity. The domain module will consist of many knowledge objects at different levels of granularity. After the intelligent tutoring system forms a profile of the student, the appropriate knowledge objects will be selected and packaged into Conclusions If designed properly and efficiently, intelligent tutoring systems can be of great benefit to distributed learning systems. Effective intelligent tutoring systems will make sure learners are given proper one-to-one instruction and attention to reach mastery as described by Bloom (1984) in his two-sigma article. The development of intelligent tutoring systems must include a team consisting of members from different disciplines to make sure that all of the expertise required for the one-to-one instruction is present. Apart from research on how to develop intelligent tutoring systems, research must be completed in other areas to continually improve distributed-learning systems. One important area of research is to determine how an intelligent tutoring system can be used for individualized learning as well as collaborative learning. This area of research is important, because some learning styles prefer collaborative learning and some content areas require collaborative learnin References Ally, M. (2000). Tutoring skills for distance education. Paper published in Open Praxis, International Council for Distance Education Journal, June, pp. 31-34. Ally, M., & Fahy, P. (2002). Using students’ learning styles to provide support in distance education. Paper published in Proceedings of the Eighteenth Annual Conference on Distance Teaching and Learning, Madison, Wisconsin, August. Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228, 4698, 456–462. Anderson, J., Boyle, C., & Yost, G. (1985). The geometry tutor. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles. Aspy, D. N., & Roebuck, F. N. (1977). Kids don’t learn from people they don’t like. Amherst, MA: Human Resource Development Press, Inc. Ausubel, D. P. (1974). Educational psychology: A cognitive view. New York: Holt, Rinehart and Winston. Berry, D. C. (1987). The problem of implicit knowledge. Expert Systems, August, 4( Chapter 7: Integrating Agents and Web Services into Adaptive Distributed Learning Environments Fuhua LinAthabasca University, Canada Larbi EsmahiAthabasca University, Canada Lawrence PoonAthabasca University, Canada Abstract This chapter discusses an integrated approach to designing and developing adaptive distributed learning environments. It presents a distributed learning environment based on agent technology and Web services technology. Agents are expected to be used as the core components in intelligent distributed learning environments because of their inherent natures: autonomous, intelligent, sociable, etc. However, to integrate agents into existing legacy learning environments or into heterogeneous learning environments, one may encounter many difficulties. They may be technical issues, economical issues, social issues, or even political issues. Web services technology, on the other hand, characterized by its standardized communication protocol, interoperability, and easy inte Chapter 7: Integrating Agents and Web Services into Adaptive Distributed Learning Environments Introduction Over the last few years, universities and colleges have made substantial progress in using the Internet to deliver courses. This is referred to as “elearning” or digital learning. This trend blurs the differences between information technology applications in education and distance education. While taking courses, students on campuses often extensively acquire distributed learning resources, communicate, and collaborate with other teachers and learners anywhere and at any time. Therefore, campus-based education and distance education, to some extent, tend to be integrated. As well, distance training is frequently used in enterprise training. Distance education and training developed rapidly over the past several years. The research on distance education and distance training has become one of the hottest fields in educational technologies. The Internet and Web-based distributed learning can potentially deliver personalized course materials and services, and therefore, are Background Distributed Learning vs. Distance Learning Traditionally, distance learning has emphasized delivering educational resources to learners in remote districts or working full-time, and providing the opportunities for open education. Distributed learning, however, enables learners to get and use educational resources distributed in different remote places. In distributed learning, the learners may be either on or off campuses. Distributed learning is based on networked learning environments. With both distance learning and distributed learning, the instructor and the learner do not have to be in the same physical location at the same time. Distributed Learning Environments A distributed learning environment in the real world can be examined in several ways. One way is to think about the main educational components and the interactions among them and their environments. Our analysis of distributed learning environments begins with an overview of the various entities comprising th Issues and Challenges In most of the existing distributed learning systems, the instructors arrange the course materials in order to cover one or more topics. For example, in Webbased distributed learning environments, the course materials are placed online to make them downloadable or visible to the students, who can use them by following the path established by the instructors. Currently, Web-based distributed learning has the following problems. First, in terms of system development, software systems for distributed learning are typically complex, because they involve many dynamically interacting educational components, each with its own need for resources, and involve engaging in complex coordination. Developing a monolithic system that could meet all requirements for every level of the educational hierarchy would be very difficult, because no single designer of such a complex system could have full knowledge and control of the system. The systems have to be scaleable and accommoda Solutions and Recommendations Adaptive Learning Environments An adaptive learning environment is a learning environment in which an automatic modification is performed at usage time, i.e., during the educational session, and is based on the learner’s characteristics. These characteristics, such as the learner’s familiarity with the educational subject and the learner’s goals and interests, are assumed to be continuously modified during the same educational session. These characteristics are not known prior to each educational session and are automatically detected by the system, through monitoring the learner’s actions. According to Jones and Winne (1992), adaptive learning environments can be viewed as the intersection of two traditionally distinct areas of research: instructional science and computational science. We call an adaptive learning environment for distributed learning a “distributed adaptive learning environment.” The main features of such environments are adaptivity and d Examples Agents and Web Services for Courses The development of agents for courses or course agents involves pedagogy, learning design, and learner modeling. The pedagogical basis of course agents can be built on two underlying educational philosophies: objectivist and constructivist. The objectivist assumes the learner is an empty vessel that can be filled with knowledge. It leads directly to an instructivist or transmissionist pedagogical approach, where the teacher fills an empty vessel, which is the student (Phillips, 1997). The other philosophy, the constructivist epistemology, assumes that the learner can build on his or her own knowledge based on an existing set of experiences, so the student is viewed as a “researcher.” A major goal of the constructivist approach is to ensure that the learning environment is as rich and interactive as possible. A course agent can be based on a constructivist learning environment, in which the student and the student’s agent can explore at will. Future Trends Agent-Supported Web Services Service-oriented computing is becoming the prominent paradigm for distributed computing and is creating opportunities for service providers and application developers to develop value-added services by combining Web services. Web services technology is currently being touted as the ideal solution to meet the requirements for the dynamic composition of Enterprise Information Systems (Yang & Papazoglou, 2000). Agents have the potential to harmonize Web services’ behaviors. The design of many software agents is based on the assumption that the user needs to specify a high-level goal instead of issue explicit instructions, leaving the how and when decisions to the agent. A software agent exhibits a number of features that make it different from other traditional components (Jennings et al., 1998), including autonomy, goal orientation, collaboration, flexibility, self-starting ability, temporal continuity, character, communication, adaptation, and Conclusions We have discussed an integrated approach to designing and developing adaptive distributed learning environments. The main objectives are to reduce the complexity of the development of distributed learning environments and to reduce the workloads of users (educators and learners) with personalized assistance in the environments where various resources are widely distributed, heterogonous, and ever-changing. Web Services technology provides a new way to integrate existing systems or applications, and the ability to access data in a heterogeneous environment. In Web Services technology, rather than building large, closed systems, the focus is on flexible architectures that provide interoperability of components and learning content, and that rely on open standards for information exchange and component integration. To reduce the information workload of educators and provide assistance to learners, distributed learning environments require that software not merely respond to re References Baylor, A. (1999). Intelligent agents as cognitive tools for education. Educational Technology, XXXIX(2), 36–41. Blackmon, W. H., & Rehak, D. R. (2003). Customized learning: A Web services approach, Proceedings: Ed-Media 2003. Chan, T. –W. (1995). Artificial agents in distance learning. International Journal of Educational Telecommunications, 1(2/3), 263–282. Dale, J., Ceccaroni, L., Zou, Y., & Agam, A. (2003). Implementing agent- based Web services, challenges in open agent systems. 2003 Workshop, July 15, Autonomous Agents and Multi-Agent Systems Conference in Melbourne, Australia, July 14–17. Dellarocas, C. (2000). Contractual agent societies: Negotiated shared context and social control in open multi-agent systems. Workshop on Norms and Institutions in Multi-Agent Systems, Fourth International Conference on Multi-Agent Systems (Agents-2000), Barcelona, Spain, June. Gavrilova, T., Voinov, A. V., & Lescheva, I. (1999). Learner-model approach to multi-agent intelligent dist Chapter 8: A Multiagent Framework for an Adaptive E-Learning System Larbi EsmahiAthabasca University, Canada Fuhua LinAthabasca University, Canada Abstract This chapter describes a multiagent system for delivering adaptive e-learning. This chapter also provides a discussion of three issues related to personalization in e-learning: technology advancement and the shift in perception of the learning process, one-size-fits-all versus personalized services, and the adaptation process. Finally, the chapter provides an overview of most known implemented systems for adaptive e-learning, as well as a detailed description of the architecture and components of the proposed multiagent framework. Chapter 8: A Multiagent Framework for an Adaptive E-Learning System Introduction to the Personalization Problem in E-Learning Computers have great potential for learning: they promise the possibility of affordable, individualized learning environments. In the early teaching systems, the goal was to build a clever teacher able to communicate knowledge to the individual learner. Recent and emerging work focuses on the learner exploring, designing, constructing, making sense of, and using adaptive systems as tools. Hence, the new tendency is to give the learner greater responsibility and control over all aspects of the learning process. This need for flexibility, personalization, and control results from a shift in the perception of the learning process. In fact, new trends emerging in the education domain are significantly influencing e-learning (Kay, 2001): The shift from studying to graduate to studying to learn: Most e-learners are working and have well-defined personal goals for enhancing their careers. The shift from student to learner: This shift h Related Works Overview of Some Implemented Systems Since the early days of Internet expansion, researchers have implemented different kinds of adaptive and intelligent systems for Web-based education. Almost all of these systems inherited their features from the two well-known types: Intelligent Tutoring Systems (Brusilovsky, 1995) and Adaptive Hypermedia Systems (Brusilovsky, 1996). Intelligent tutoring research focuses on three problems: curriculum sequencing, intelligent analysis of learner’s solutions, and interactive problem-solving support. Adaptive hypermedia systems research focuses on adaptive presentation and adaptive navigation support. In this section, we briefly present some implemented systems that use one or more of these concepts. For more details on these systems, the reader can refer to the cited references. ELM-ART (Weber & Specht, 1997; Weber & Brusilovsky, 2001): This is an on-site intelligent learning environment that supports example-based programming, intelligen Why Use Intelligent Agents for Adaptive E-Learning? The main objective of e-learning systems is to enable individually subscribedto learning services to be delivered to their associated users whenever they request them, and wherever the users are, in a customized form that matches their profile. Thus, intelligent mobile agents have been introduced to provide this kind of dynamic service provisioning and management. The agents’ technology has several advantages for implementing new services on distributed systems. In fact, this technology enables these systems to distribute the functionalities in small, reproducible, and distributed software entities. It also allows for a clear and easy separation between their internal, private knowledge, and their interface toward the external world and other agents. The e-learning services provisioning and management fits well for exploiting the agents’ properties. Agents’ autonomy: Allows for making decisions on service access, the interfaces’ confi Multiagent Framework for Course Personalization Context The design basis for this framework is a university e-learning environment (i.e., Athabasca University), where learners are taking courses in an asynchronous mode (grouped or individual). In this context, most learners are working and have a personal need to enhance their careers. As well, these learners often have a long-term learning plan and prefer a flexible and individualized learning environment. A significant percentage of these learners is always mobile and needs to have access to their courses from everywhere, anytime, and by using different devices. Thus, the developers of this project must consider the following factors: Asynchronous e-learning Mobile users Multidevices environment Adapted courses Personalized interfaces that offer the same look and feel System Architecture In this context, we are proposing a multiagent architecture for implementing an e-learning system that offers course personalization and supports mo Conclusions The proposed multiagent architecture offers the advantage of being more flexible and scalable than other architectures. The course adaptation may be done either in the server or the client, depending on the user’s needs. This architecture also offers a dynamic adaptation either for the course content or the interface, because the tutor agent is in permanent communication with the user agent and the terminal agent. Furthermore, for the course content, the system is open to third-party providers. References Brusilovsky, P. (1995). Intelligent tutoring systems for World-Wide Web. In R. Holzapfel (Ed.), Poster Proceedings of Third International WWW Conference (pp. 42–45). Darmstadt. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129. Brusilovsky, P. (1999). Adaptive and intelligent technologies for Web-based education. In C. Rollinger, & C. Peylo (Eds.), Special Issue on Intelligent Systems and Teleteaching, Künstliche Intelligenz, 4, 19–25. Brusilovsky, P. (2003). Adaptive navigation support in educational hypermedia: The role of learner knowledge level and the case for meta-adaptation. British Journal of Educational Technology, 34(4), 487–497. Brusilovsky, P., & Eklund, J. (1998). A study of user model based link annotation in educational hypermedia. Journal of Universal Computer Science, 4(4), 429–448. Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based ed Chapter 9: A Language for Specifying Agent Systems in E-Learning Environments Hong LinUniversity of Houston-Downtown, USA Abstract In this chapter, we use the Chemical Reaction Metaphor (Banatre & Le Metayer, 1990, 1993, 1996) to model the interactions among program units, including the agents, clients, servers, and databases, in a multiagentbased e-learning system. Through case studies, we demonstrate that the Gamma language (Banatre & Le Metayer, 1990; Le Metayer, 1994) is suitable for specifying a multiagent system, particularly, because an agent’s properties, such as autonomy and mobility, can be captured concisely. The use of the high-level specification paves the way for solving architectural-design issues in building an e-learning environment. The Gamma specification of an agent system can be implemented in a hierarchical running environment, which is composed of nodes in different levels of a tree. Interactions among agents can be implemented in a unified mechanism for synchron Chapter 9: A Language for Specifying Agent Systems in E-Learning Environments Introduction Gamma (Banatre & Le Metayer, 1990, 1993, 1996) is a kernel language in which programs are described in terms of multiset transformations. It is a high- level programming language in which parallelism is left implicit and which is especially suitable for use with the data parallel paradigm. Existing work has demonstrated its significance for the construction of massively parallel programs. With the Gamma programming paradigm, programmers can concentrate on the logic of problems to transfer the tuple space (this terminology is borrowed from Linda, which shares some ideas with Gamma) (Carriero & Gelernter, 1989; Ma et al., 1995), and are free from considering the execution environment. Gamma has allowed for elaboration of the chemical-reaction model, such as the Chemical abstract machine (Cham) (Berry & Boudol, 1992), higher-order Gamma (Le Metayer, 1994), structured Gamma (Fradet & Le Metayer, 1998), and Multran (Ma et al., 1995). The application of the chemical-reaction met Background With the fast growth of the Internet, it is now being used in every aspect of human life. In particular, the online delivery of courses has led to a brand new field that all educators, both the advocates of traditional education and the developers of new methods, have to face—e-education, in which the learning process occurs in a distributed manner. Not only can the instructors and learners be geographically distributed, but also the sources of courses and the assembly and delivery of instructional material can be managed dynamically by coordinating distributed entities. How can agent systems make distributed online learning possible? First, because of their adaptability, agent systems can adjust the instructional material according to the changing demands of a learner. An agent can reason based on its existing knowledge and past experience to determine the best action in the given circumstances. Moreover, it can accommodate new situations by learning. Second, as an agent is The Chemical Reaction Model Gamma A distributed system involves entities in distributed sites that communicate with each other through communication links. The complexity of designing such a system is an issue under research. The designer of the system cannot afford the burden of decomposing a program into a large number of individual tasks. Therefore, the Gamma language was proposed (Banatre & Le Metayer, 1990; Le Metayer, 1994) to create the kind of high-level architectural design in which parallelism is left implicit. A Gamma program is composed of a set of rules governing the interactions among underlying program units, termed “reactions,” which are analogous to chemical reactions. The underlying data structure is modeled by a multiset. No global control is imposed on the way multiset elements are selected to ignite the reaction. The execution of the program proceeds until no more reactions can take place, and the multiset at that point represents the result of the computation. A “ Modeling Agents with Gamma Modeling Interagent Communications and Agent Migration As a high-level language, Gamma can succinctly model computations. Local computations within agents can be described by using methods used in previous research. However, to model agent systems, we need to find a mechanism for describing interagent communications and agent migration. In the framework of the higher-order Gamma, interagent communications and agent migration can be merged by viewing interagent messages as an inert program. For example, the following rule describes a message transmission if M is a message, and an agent migration if M is a program: M: E1, M’ : E2®[M, M’ ]: E2, ¬ L(E1, E2) Λ C(M, M2 ), where E1, E2 denotes two running environments, L(E1, E2) is a communication link between E1 and E2 , and C(M, M’ ) is the condition causing the message transmission or agent migration. As stated before, agent systems do not have a single type or definition. The following is a collection of subc Specifying Multiagent Systems in E-Learning Environments Let us examine the multiagent system for course maintenance and recommendation that was designed in the literature (Lin et al., 2003). In this system, online course materials, including all textbooks or e-books and study guides, plus the project handouts, can be downloaded and installed on students’ hard drives. Students only need to be online to post material on the conference, do the quizzes, or send and receive e-mails pertaining to the course. The online course materials are updated often in order to keep them as current as possible, especially in some rapidly changing fields, like “computing and information systems.” Because of the complexity of the materials, and the short development cycles within which they are produced, the course instructor should make the necessary adjustments time to time for the benefit of the students. Whenever there is a significant change in the content of several designated Web pages of online co From Specification to Implementation The above section demonstrates that Gamma language can be used as a specification language of agent systems. In this section, we discuss implementation issues. Because the Gamma language is a high-level language designed for program specification, especially for specifying distributed systems, and because this language does not have any sequential bias, it has no straightforward implementation that addresses the efficiency of the target program. Structured Gamma (Fradet & Le Metayer, 1998) language has been proposed to address the implementation issue by introducing an “address” component into the multiset, thereby furnishing a mechanism for representing data structures. Also, Lin et al. (1997) proposed a transformational method for implementing a Gamma program, but in spite of these attempts, it has never been efficiently implemented with enough automation. Therefore, the Gamma language is applied as a specification language mainly to areas of coor Discussion and Directions for Future Research The benefits of using Gamma as a specification language for agent systems is not limited to the method it provides for accurate agent system design. Because Gamma is a functional programming language, it enables us to reason about the properties of the specified system (Le Metayer, 1996; Hankin et al., 1993a,b; Reynolds, 1996; Ma et al., 1994; Ma & Orgun, 1995; Lin et al., 1997, 1998; Lin & Chen, 1998). In Reynolds (1996), Ma et al. (1994), Ma & Orgun (1995), and Lin et al. (1997), temporal logic is used to define the semantics of the Gamma language. Temporal logic semantics has also been used to facilitate a program-transformation method that provides a way to implement the Gamma language. Other studies of the semantics of the Gamma language include the study of categorical semantics (Lin & Chen, 1998; Lin et al., 1998). In Le Metayer (1996), the Gamma language is used to reason about the type of software architecture. More research will b Conclusions We have used the Gamma language as a formal language for the specification of agent systems. Through case studies of various agents, we demonstrated the feasibility and benefits of using Gamma as a specification language for multiagent systems, in light of how architectural design can be streamlined succinctly. A case study is also done in specifying a multiagent based e-learning system for course material maintenance. We studied implementation issues and proposed a method for building a multiagent system from its Gamma specification. Our approach involves using a hierarchical running environment comprised of first- class nodes and controlling nodes that implemented the nesting configuration structures in the Gamma language. Synchronization issues were solved by allowing controlling nodes to monitor and control the execution of the nodes on the lower levels. References Allen, R., & Garlan, D. (1994). Formalising architectural connection. In Proceedings of the IEEE 16th International Conference on Software Engineering (pp. 71–80). Banatre, J. -P., & Le Metayer, D. (1990). The Gamma model and its discipline of programming. Science of Computer Programming, 15, 55–77. Banatre, J. -P., & Le Metayer, D. (1993). Programming by multiset transformation, CACM, 36(1), 98–111. Banatre, J. -P., & Le Metayer, D. (1996). Gamma and the chemical reaction model: Ten years after. In J. M. Andresli, & C. Hankin (Eds.), Coordination programming: Mechanisms, models and semantics. Imperial College Press. Berry, G., & Boudol, G. (1992). The Chemical Abstract Machine. Theoretical Computer Science, 96, 217–248. Carriero, N., & Gelernter, D. (1989). Linda in context. CACM, 32(4), 444–458. Flores, R. A., Kremer, R. C., & Norrie, D. H. (2001). An architecture for modeling Internet-based collaborative agent systems. In T. Wagner, & O.F. Rana (Eds.), Infrastructure for Chapter 10: A VR-Based Virtual Agent System Timothy K. ShihTamkang University, ROC Ying-Hong WangTamkang University, ROC Yung-Hui ChenTamkang University, ROC Abstract Agent technology can be used to represent individuals participating in a virtual university. Avatars are virtual actors on behalf of students and instructors navigating in a three-dimensional (3D) virtual campus. This chapter presents a system based on virtual reality (VR) technology as well as agent technology that enables online discussions via different real-time communication channels. The system has a generic interface, which includes five scenes of a virtual university, as well as a set of plug-and- play communication agent tools. Each user is maintained by an intelligent agent that controls the navigation behavior based on a rule-based computation. Behaviors of each student are restricted and guided by intelligent agents. The system can be extended for the construction of any virtual university with 3D campus and o Chapter 10: A VR-Based Virtual Agent System Introduction In line with the growing popularity of distance education, we developed a series of distance-learning software systems (Deng, 2002; Shih & Chang, 2001; Shih, 2002; Shih, 2001; Holt, 1995) based on Internet and Web browsers. These systems were used in our university among different departments. On the other hand, 3D graphics and the associated real-time communication technologies were developed and used in video games. Video games get the attention of our younger generation. In addition, VR and computer graphics techniques have been used in education and training (Sala, 2002; Sala, 2003; Garnett, 1999; Zoller, 1990). VR can also help constructivist learning (Winn, 1993). We try to combine VR and communication technologies, with an educational theory, to develop a VR-based situation learning environment, which facilitates and encourages students using online discussions. We integrate our distancelearning systems developed under a generic VR-based communication inter- face. W The Construction of the 3D Campus Scene Virtual reality technology is used widely in video games, multimedia tutorials, and simulations. We aim to develop a 3D campus via the VR technology. The 3D campus will include five scenes, as discussed in the following: Open Plaza: The open plaza is a wide open area where students first enter the virtual university. This area allows students to walk through different facilities and scenes. The preliminary scene will include trees, chairs, stages, trails, and four entrances to other scenes in the 3D virtual campus. Library: The library allows students to talk to an online librarian and ask questions such as how to search for references and how to check out reserved books and notes from an instructor. Preliminary scenes will include a check-out counter, bookshelf, desk for online searching, etc. Faculty Office: The faculty office allows instructors to hold office hours. The preliminary office will include tables, chairs, and a shared white board f VR-Based Motion Detection and Behavior Understanding Students in a virtual university have individual learning profiles, which may include exam records, Web site navigations, chat room discussions, and even their behaviors in a virtual campus. Motion detection of students in a 3D campus can be easily obtained. However, it is difficult to analyze the semantics of student motion. We aim to develop a behavior supervision machine, based on finite state automata, to properly guide students while they are on the campus. In the virtual campus, five scenes are constructed. Theoretically, it is possible for students to go from one location to another without any restrictions. In the real world, learning activities may proceed according to some templates of actions. For instance, after a student attended a class, he or she may go to the library to check out a reserved reference assigned by the instructor. And, the student needs to make an appointment with the instructor before he or she can solv A Declarative Specification Language for Learning Behavior Supervision As we discussed in the main project, in order to facilitate the application of the scaffolding theory, we developed a declarative specification language. An instructor can use a graphical user interface to design a specification program, which allows adaptive course contents and adaptive distance-learning tools. The specification program is an underlying engine, which performs a bookkeeping duty, for the instructor to use to analyze individual learning behavior via the activities of students on a virtual campus. Even though the specification language looks complicated, the programming is through a dialogue window. Thus, users will not need to know the syntax of such a language. However, the dialogue box, which was carefully designed, will allow different options. The specification program is generated after the dialogue box is completed. We discuss the syntax and semantics of the declarative specification language, Conclusions The proposed system demonstrates the preliminary results of an ongoing distance-learning research project being worked on at several universities. We have implemented the generic user interface as well as a state machine engine that runs the specification language. Interactions via communication tools, such as videoconferencing and chat room discussions are possible due to the support from the underlying system provided by Microsoft. The preliminary system shows the feasibility of using scaffolding theory in distance education, which is considered the most important contribution of this paper. Behavior supervision is another contribution of this research. We are working on a French literature courseware with which to use the proposed system. A few tools must be developed before being integrated. The FAQ auto-reply system and the chat room participation tools are still under development. We hope that, in the near future, VR technology can be used as another channel for dista Acknowledgment We would like to thank Yuan-Kai Chiu and Chia-Tong Tan for their help in implementing the system. Without their help, our research results would not be completed. References Deng, L. Y., Shih, T. K., Huang, T. -S., Liao, Y. -C., Wang, Y. -H., & Hsu, H. -H. (2002). A distributed mobile agent framework for maintaining persistent distance education. Journal of Information Science and Engineering, Special Section on Parallel and Distributed Systems, 18(4), 489–506. Garnett, P. J., & Treagust, D. F. (1992). Conceptual difficulties experienced by senior high school students of electrochemistry: Electrochemical (galvanic) and electrolytic cells. Journal of Research in Science Teaching, 29(10), 1079–1099. Holt, P., & Gismondi, J. (1995). Integrating virtual spaces into open learning systems. Innovations in Education. Minot, ND. Sala, N. (2002). VR as an educational tool. In Proceedings of the International Conference on Computers and Advanced Technology Education (CATE) (pp. 415–420). Cancun, Mexico. Sala, N. (2003). Hypermedia modules for distance education and virtual university: Some examples. International Journal of Distance Education Technologies, Chapter 1: A Human Collaborative Online Learning Environment Using Intelligent Agents Chapter 2: Intelligent Agents Supporting Distributed Collaborative Learning Chapter 3: Privacy and Trust in Agent-Supported Distributed Learning Chapter 4: Intelligence in MAS-Based Distributed Learning Environments Chapter 5: Knowledge Management for Agent-Based Tutoring Systems Chapter 6: Intelligent Tutoring Systems for Distributed Learning Chapter 7: Integrating Agents and Web Services into Adaptive Distributed Learning Environments Chapter 8: A Multiagent Framework for an Adaptive E-Learning System Chapter 9: A Language for Specifying Agent Systems in E-Learning Environments Chapter 10: A VR-Based Virtual Agent System Chapter 1: A Human Collaborative Online Learning Environment Using Intelligent Agents Chapter 2: Intelligent Agents Supporting Distributed Collaborative Learning Chapter 3: Privacy and Trust in Agent-Supported Distributed Learning Chapter 4: Intelligence in MAS-Based Distributed Learning Environments

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