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Dal web 2.0 all'E-L 3.0

Dal web 2.0 all'E-L 3.0



Introduzione all'incontro su EL e KM (15/7/08)

Introduzione all'incontro su EL e KM (15/7/08)



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    Dal web 2.0 all'E-L 3.0 Dal web 2.0 all'E-L 3.0 Presentation Transcript

    • Intelligent Learning Environment Concept
    • LMS – ILE Goals
      • To create a platform able to support :
        • Agents with “ profilable subject matter “ expert knowledge
        • Relief Tutors from 1° level “ Subject matter “ tutoring activity
        • Students / Work Groups (LE) with 1° level “ Subject matter “ tutoring
        • Interpret the “tutoring role” according to :
          • Knowledge / Subject matter profile
          • Pedagogical profile in conjunction with the “learning entity (LE)” profile
        • AI Engine input
          • LMS tracking data
          • LSA , or any type of system interpretable, external input
          • Profiles
          • Manual intervention from Experts, Tutors etc.
    • LMS – ILE Design issues
      • Static Design strives for a simple, elegant, efficient solution to a single situation.
      • Outside of that situation the design is useless
      • Adaptation strives to just survive in a constantly changing situation.
      • Adaptation is continuously making design decisions
      Requirements Design Implement Test Water Fall Design Process Impl Impl Adaptive Control Loop Implementation Loads Capacity Cost QoS Adaptive Control Policy Conditions Conditions
    • LMS – ILE Environment Global Architecture of the ILE Platform
    • LMS – ILE Environment
      • OLAT Java based LMS performing functions like:
        • Student , Teacher, Authors administration
        • Group administration
        • E-Course administration
        • General access control
        • Global community functions like Forum, Wiki, Blog
        • Extended tailored functions like e-Portfolio etc.
      • AI coaching / tutoring Engine
        • Learning entity Agent (real-time student / group profiler) Curriculum Subagent (generating and adapting learner curriculum)
        • Knowledgebase Agent (Subject matter related) Pedagogical profiler Coach / Tutoring Subagent Subject matter profiler
        • Presentation Agent (Dynamic session initiator)
        • Pedagogical Agent
        • Coaching/Tutoring administrator Agent
      • Authoring Middleware
        • AIML / UML / SOAP utilities
        • AI ILE Authoring tools
        • Global Knowledgebase management utilities
      • Standalone e-Course development environment
      • AI simulation / Test utilities
      ILE Components
    • OLAT – ILE INTEGRATION ILE-AI Push/Pull Servlet ILE-AI LMS Communicator AI - Engine
    • LMS – ILE Environment Plugins Blackboard (Method for inter Agent Communication) Agent Publish (Push) Subscribe (Pull) Message Queue
    • LMS – ILE AI Framework component servic e BB Behavior effecter coordinator sensor component service servic e component library Agent BB Behavior effecter coordinator sensor component component service servic e library Agent Abstracted Environment System specific Variable Boundary Application domain specific Cougaar Agent Reference Model Framework Infrastructure
    • LMS – ILE AI Framework
      • Symbolize domain knowledge
      • Continuously executing
      • Responding to dynamic environment
      • No dependencies on other Plugins
      • Publish all relevant information to the blackboard
      Plug-in Functionality Allocators Assessors Cougaar Agent Data User Interface Expanders
    • Architectural Mapping Sensor-Based Control Loop Model-Based Control Loop Cognitive Control Loop Model Policy Situation inference rules days to minutes secs to msecs Network Disk management plane data plane Sensor/ Activity Proxy Agents Real-time Optimizer Agents processing status coordination resource status coordination resource trends coordination Cognitive Learner Agents processing. trends coordination Situation Predictor Agents processing pattern coordination resource pattern coordination Sensor/Activity Proxy Agents Processing Units CPU
    • Architectural Mapping
      • Application
      • Functional modules (oval shaped)
      • Underlying distributed environment
      • Sensor / Activity to control loop coordination
      • Evolving degree of human involvement
      • Cougaar
      • Agent societies
      • Cougaar environment
      • Agent coordination
      • Transitioning of control loops human to automation
      architectural mapping
    • LMS – ILE Environment Agent Agent Coordination Artifact (CA) Agent Defines roles Agent Role-players Shared state
      • Coordination Artifacts: CAs
        • Are first-class entities in MAS
        • Define explicit roles for role-players
        • Offer shared state between the role-player & the CA
        • Coordinate behavior among role-players
        • Have distributed implementation
    • Dynamic Re-Planning and Execution Monitoring
      • Negotiate between agents to iteratively improve plan
      • Repetitively recheck assumptions : Constraints, Data, Policies, Metrics
      • Updates from data sources as time progresses
      Data Source Real-Time Data Fusion Self-Assessment Allocation Results Reallocations Task Allocations
    • Supporting Adaptation in the System Life Cycle Metric Service Management Society Agent Services Coordination Run Environment Configuration Rules Society Configuration Rules Deploy Components Binders/Aspects Plug-ins Configure Event Driven SOA (Service Oriented Architecture) Data Driven Blackboard Knowledge Rep Program Environment Agent Development Phase IDE Application Plug-ins Deploy Rules Spec Tool Run Server Cougaar Middleware Society Monitor
      • Thank you for your attention