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ehaviors queries User Agent user ontology ontology. The corresponding class Department- Agent has a similar structure as UserAgent: In its setup()-Method DAMLJessKB is used to load the department ontology, specific rules and the necessary Jess queries. Each query can be executed in a corresponding agent method. One example is getNewerDocs(), which yields all documents related to a course which are newer than a specified date. 4.2 Agent Behavior and Communication Request/Query-If rulesss An agent must be able to execute several parallel tasks in response to different external events. In JADE all agent tasks are modeled as objects of the Behavior subclass, which determine the reactions of an agent: e.g. when it receives a message, and how it reacts on requests from another agent. The JADE-method addBehavior() adds a behavior to the task queue of a specific agent. Behaviors a registered in an agent’s setup()method or on response to an user event. In a round-robin policy a scheduler executes the action()-method of each behavior in the task queue. If the action()-method is finished, the done()-method is invoked. If it returns true, the task is removed from the event queue. To model cyclic tasks done() returns always false. Details about behaviors can be found in (Bellifemine et al., 2002). For example, the following behaviors are defined in the E-learning system. The user and the department agent use the RegisterAtDF behavior, which registers an agent with its name and type at the agent platform. The user agent uses the UA_SearchDepartment- Agent-behavior to ask the name service of the platform, the Directory Facilitator (DF) for all Inform/Inform-done ruless Figure 3: Agent structure SEMANTIC E-LEARNING AGENTS Department Agent depart. ontology behaviors queries department agents, and to establish a connection to them. The UA_SendRequest-behavior requests information from the department agent. This behavior object is created by an event on the agent GUI. According to a parameter content (e.g. SEND_DOCUMENTS) the user agent collects the necessary request parameters, e.g. the courses of a user, and sends them via an ACL message to the department agent. Furthermore a command string (here: DOCUMENTS) is set to specify the request. void action() { ... if(content== SEND_DOCUMENTS){ ... parameters.setCourses( this.getCourses()); ... msg.setContentObject(parameters); msg.setLanguage(DOCUMENTS); ... } msg.addReceiver(agent); myAgent.send(msg); 243 The UA_ReceiveRequests-behavior waits in an infinite loop for messages from a department agent. If a message arrives it is analyzed and the results are sent to the agent’s GUI. The department agent uses the DA_SearchUser- Agent-behavior to get all user agents, and to establish a connection to them. The DA_ReceiveRequest-behavior analyzes arriving messages from user agents. It extracts the command string and the parameters of the message, to execute the specified query. Then the query results are packed into a message and returned to the corresponding user agent.
244 ENTERPRISE INFORMATION SYSTEMS VI 5 CONCLUSION In this paper, we have described the use of Semantic Web languages and agent technology for building an intelligent advisory system for E-learning environments. Our goal is to create and deploy semantic E-learning agents capable of supporting university students in successfully organizing and performing their studies. In the project we have developed a software architecture, which integrates Semantic Web and Intelligent Agent technologies. Due to the use of Semantic Web languages the developed knowledge models can easily be used in distributed systems and shared among software agents via the Internet. The major difficulty encountered was the integration of the different concepts – on the one hand the knowledge base written in RDF and DAML+OIL, on the other hand the inference engine JESS and the agent environment JADE. Further problems emerged from the unsatisfactory tool support for developing the ontology and the concrete instances of the ontology. However, after the mentioned problems were solved we could implement a prototype system, where the agents were able to reason upon the knowledge base in the desired manner. Actually the migration of our system to the upcoming W3C standard language OWL is under work. REFERENCES Berners-Lee, T., Hendler, J., Lassila, O., 2001. The Semantic Web. Scientific American. Bellifemine, F, Giovanni, C., Trucco, T., Rimassa, G., 2002, JADE Programmers’s Guide, http://sharon.cselt.it/projects/jade/, retrieved October, 2003. Dunkel, J. Holitschke, A., Software Architecture (In German), 2003. Springer Verlag. Bruns, R., Dunkel, J., von Helden, J., 2003. Secure Smart Card-Based Access To An eLearning Portal. In ICEIS’03, 5th International Conference on Enterprise Information Systems. ICEIS Press. CCTT - Center for Curriculum, Transfer and Technology, 2002.http://www.edutools.info/course/compare/all.jsp, retrieved October, 2003. Cuena J., Ossowski S., 1999. Distributed Models for Decision Support. In: Weiß (ed.): Multi-Agent Systems — A Modern Approach to DAI. MIT Press, 459–504. DAML-The DARPA Agent Markup Language Homepage: http://www.daml.org, retrieved October 10, 2003. DAML API, 2003. http://codip.grci.com/Tools/Components.html, retrieved October, 2003. Friedman-Hill, E., 2000a. JESS, The rule engine for the Java platform,. http://herzberg.ca.sandia.gov/jess/ retrieved October, 2003. Friedman-Hill, E., 2000b, Jess. The Rete Algorithm, Sandia National Laboratories, http://herzberg.ca.sandia.gov/jess/docs/52/rete.html, retrieved October, 2003. FIPA - Foundation of Intelligent Physical Agents, 2003. www.fipa.org, retrieved October, 2003. Horrocks, I., Hendler, J. (eds.), 2002. The Semantic Web, First International Semantic Web Conference, Sardinia, Italy, Springer LNCS 2342. Hewlett Packard Labs: Jena Semantic Web Toolkit, 2003. http://www.hpl.hp.vom/semweb, retrieved October, 2003. Kopena, J. Regli, W., 2003, DAMLJessKB: A Tool for reasoning with the Semantic Web. IEEE Intelligent Systems, 18(3). Ossowski, S., Hernández, J., Iglesias, C.A.; Fernández, A., 2002. Engineering Agent Systems for Decision Support. In: Engineering Societies in an Agent World III (Petta, Tolksdorf & Zambonelli, eds.), Springer- Verlag. Ossowski, S., Omicini, A., 2002. Coordination Knowledge Engineering. Knowledge Engineering Review 17(4), Cambridge University Press. SWI-Prolog, 2003. http://www.swi-prolog.org, retrieved October, 2003. WWW – The World Wide Web Consortium, 2003a. RDF Primer – W3C Working Draft 05 September 2003: http://www.w3.org/TR/2002/WD-rdf-primer- 20020319/, retrieved October 10, 2003. WWW – The World Wide Web Consortium, 2003b. OWL (Web Ontology Language): http://www.w3.org/TR/owl-ref/ , retrieved October 10, 2003. Wooldridge, M.; Jennings, N., 1995. Intelligent Agents - Theory and Practice. Knowledge Engineering Review 10 (2), pp. 115–152.
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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