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ContentsAbstract 1Acknowledgements 2Contents 3Introduction 6Chapter 1: State -of-the-Art 91 Recommender systems1.1 Background991.2 Examples1.3 A profiling technique10112 Agents 122.1 Overview 122.2 Multi-agent systems (MASs) 132.3 Examples of agents and MASs 172.4 Existing tools 192.5 Design 223 Metadata 243.1 Definition 243.2 Roles and use 243.3 Storage 253.4 Interoperability issue 254 Perspectives 28Chapter 2: Contribution 291 Our approach 292 Expected outcomes 292.1 User navigation assistance. 292.2 Feedback for administrators and content providers. 303 Principles of our solution3.1 User perspective30313.2 Resource perspective3.3 Business intelligence generation31324 Consequences 32Chapter 3: Application Design 331 Structure overview 332 Navigation System Interface 343 Agents Component 34MSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 3
3.1 User Agents Component 353.2 Node Agents Component 354 Standard User Profiles Component 355 Business Intelligence Component 356 Non-generic part 366.1 Choice of Navigation System6.2 Consequences on the application3637Chapter 4: Implementation 381 Implementation strategy 382 Agent infrastructure level 392.1 Agent communication 392.2 Agent management 393 Agent framework level 413.1 Agent design 413.2 Synchronized inter-activity communication 424 Application level 444.1 Overview 444.2 User Agent Proxy 444.3 User Agent Maker 464.4 User Agent 464.5 Business Intelligence Component 48Chapter 5: Future Work 511 Application outputs 512 Explicit user inputs 513 Validation of the approach 524 Genericity checking 525 Application implementation 526 Improvement of the agent system 53Conclusion 54Index of terms used 55Appendix A: Addendum on Industrial Issues 56Learning objects 561. The promises of e-learning2. The necessary emergence of learning objects5656Appendix B: SQL Tables Design 581 Table UP (User Profile) 582 Table UPNode 583 Table UPLink 58MSc EMOOSE Thesis – http://www.emn.fr/EMOOSE 4
Page 1: Vrije Universiteit Brussel - Belgiu
Page 6 and 7: (4) Table UP_SUP (matches User Prof
Page 9 and 10: content providers’ interpretation
Page 11 and 12: - Active profiling consists in aski
Page 13 and 14: 2 Agents2.1 Overview2.1.1 Definitio
Page 15 and 16: (1) each agent has incomplete infor
Page 17 and 18: MASs need an organization for speci
Page 19 and 20: However, the usefulness of personal
Page 21 and 22: they often provide implementation o
Page 23 and 24: 2.5 Design2.5.1 The DIMA designIn [
Page 25 and 26: 3 MetadataDuring the earlier invest
Page 27 and 28: This requires some agreement and st
Page 29 and 30: 4 PerspectivesRecommender systems m
Page 31 and 32: way Lego blocks should be assembled
Page 33 and 34: usiness intelligence is generated.
Page 35 and 36: important than memory space optimiz
Page 37 and 38: The graph is then formatted to be v
Page 39 and 40: Chapter 4: Implementation1 Implemen
Page 41 and 42: (1) A Name Server (NS) maps every a
Page 43 and 44: the agent terminates, the thread is
Page 45 and 46: 4 Application level4.1 OverviewThe
Page 47 and 48: 4.3 User Agent MakerThis agent has
Page 49 and 50: Thus the profile, node and link cla
Page 51 and 52: BIProviderR : Provide_Business_Inte
Page 53 and 54: to enable users to explicitly speci
Page 55 and 56:
ConclusionA “minimalist” approa
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Appendix A: Addendum onIndustrial I
Page 59 and 60:
Appendix B: SQL Tables Design1 Tabl
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Appendix C: Conference PaperA paper
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very large repositories of learning
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popularity of the link (the bigger
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Appendix D: Source Code1 Agent infr
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public static bool ProcessIsMainHos
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string path = MakePath(".", Middlew
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System.Messaging.MessageQueue queue
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public static NameServer GetNS(){if
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}}}table[agentId] = address;}}/// /
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****** NON-STATIC PART ******// Tab
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}}}return (AgentId) first;using Sys
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{}return Subject.GetHashCode() + Na
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name in order to allow for the retr
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}// Start agent's main Thread.Threa
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}DirectoryFacilitator.GetDF().Unreg
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{/// /// Allows for specific conver
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}Monitor.Exit(aLock);public overrid
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Allows for synchronized Message exc
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esource./// /// public string GetMa
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}}private void RunTerminationExamin
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}private Message RequestUACreation(
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}msgQueue.AddFilters(new MessageCat
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Message received: interrupt preterm
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19. Class Architecture.DBManagerAg
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}}connection.Close();/// /// For sa
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using System.Collections;namespace
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namespace Architecture.Profiles{usi
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private UP(DataRow row) : this(newU
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}DataRow[] rows = Row.GetChildRows(
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}adapter.Update(Data);//***********
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internal static readonly string LAS
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}}Row[OCCURRENCES] = Occurrences +
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}get{}return Data.Rows.Find(_name);
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private static SqlDataAdapter adapt
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}foreach (ILink link in links){if (
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}}}private DataRow Row{get{return D
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18. N. Good, et al. Combining colla
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49. D. Derbyshire, et al., Agent-Ba
Page 139:
83. Council for Higher Education Ac
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