Simulation Learning is a frequent practice to conduct near-real, immersive and engaging training sessions. AI Planning and Scheduling systems are used to automatically create and supervise learning sessions; to this end, they need to manage a large amount of knowledge about the simulated situation, the learning objectives, the participants’ behaviour, etc.
In this paper, we explain how Linked Data and Semantic Web technologies can help the creation and management of knowledge bases for Simulation Learning. We also present our experience in building such a knowledge base in the context of Crisis Management Training.
1. A Linked Knowledge Base for
Simulation Learning
Irene Celino and Daniele Dell’Aglio
CEFRIEL, ICT Institute, Politecnico di Milano
http://swa.cefriel.it, http://iricelino.org
http://www.pandoraproject.eu
Linked Learning Workshop at ESWC 2011 – Heraklion, Greece – May 29th 2011
2. Simulation Learning
Characteristics and aims
Improve Soft Skills, especially Decision-Making
Effective and Engaging Learning
Re-create a near-real environment
Cheaper than on-the-field training
More effective than table-top training
PANDORA project scenario: Crisis Management Training
Trainees are "Gold Commanders": strategic roles in the decision-
making process who monitor a crisis situation and takes decision to
be dispatched to tactical agents ("Silver Commanders")
A Linked Knowledge Base for Simulation Learning 2 Linked Learning Workshop at ESWC 2011
3. Knowledge in Simulation Learning
Requirements in terms of Knowledge Management
Scenario descriptions
Events and stimuli for the trainees
Storyboards for the learning sessions
Multimedia assets
Supporting documents and guidelines
Trainees description and behaviour/decisions
Learning session monitoring
Role of the Exercise Director (Trainer) in traditional SL
Define the exercise storyboard and ensure its planned execution
Feed information to the trainees and let them make informed decisions
Inputs at pre-set intervals, regardless of the previous responses; or
By timing the inputs on the basis of the trainer judgment and experience
Facilitate rather than orchestrate the exercise
Minimal trainer intervention to let trainees recognise and correct problems
Decide how much advice to trainees
A Linked Knowledge Base for Simulation Learning 3 Linked Learning Workshop at ESWC 2011
4. The need for a smarter solution...
An automated way to "play" the storyboard
Planning and Scheduling technology to support feeding the stimuli
to trainees
A consistent way to interlink and maintain knowledge
Linked Data and Semantic Web technologies to model and
maintain the knowledge for the simulation storyboards and the
training sessions recording
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5. Simulation Learning Architecture
Simulation Trainer
PLANNING
Behaviour Simulation Learning
SENSING DELIVERY
Learning
Simulation Learning Environment
Trainees
A Linked Knowledge Base for Simulation Learning 5 Linked Learning Workshop at ESWC 2011
6. Modelling
Behaviour Learning
SENSING DELIVERY
Trainee profile Asset modelling
Registry data Scenario description
Psychological data Audio and video assets
Before the training NPC (Non-Player
session (questionnaire) Characters)
During the training
session (self-
assessment)
Physiological data (this part of the modelling is more
Holter monitoring similar to "usual" learning objects
(heartbeat) modelling)
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7. Planning Knowledge
Simulation
PLANNING
Simulation Events
Different communication media (email, video news, phone calls, ...)
Trainees' Decisions
Causality
Timeline-based Planning
"Synchronizations" (or Planning Rules)
On the basis of a happened event, what other events can be triggered
on what conditions and in which timeframe
tpl:Event tpl:hasPlanningRule
tpl:hasValue (isReferenceOf)
tpl:hasRuleRelation
tpl:hasTarget tpl:PlanningRule
tpl:RuleTarget tpl:RuleRelation
(Synchronization)
A Linked Knowledge Base for Simulation Learning 7 Linked Learning Workshop at ESWC 2011
8. Provenance Tracking for Planning
Learning Session
wasGeneratedBy(R)
used(R)
A1 P A2
Event or Decision Event or Decision
wasControlledBy(R)
Completion and Inference rules
of the OPM
Consistency checking of the
Ag simulation modelling (at design
time)
Trainees or NPC
Analysis of average trainee
decisions to discover common
behaviours (after learning
sessions)
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9. A Linked Knowledge Base
Simulation Learning Environment
other system components
Training GUI
Simulation
Simulation
Trainees Planner
Web of
Linked Data
Trainer
Knowledge
GUI
Trainer
Base
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10. Some notes on the deployment
Pandora Environment Pandora Environment
Local Installation 1 Local Installation 2
Pandora Box Pandora Box
Local Local
KB Global KB
KB
Web of Linked Data
LAN boundary LAN boundary
Distinction between local ald global uner two perspectives:
Private vs. public (preserve confidentiality and privacy)
Commercial vs. non commercial (scenario description is a business
asset for learning organizations!)
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11. Lessons learned
Knowledge technologies are crucial for simulation learning
Linked Data technologies can be exploited to
Reuse and link to pre-existing knowledge
Share simulation modelling between different systems
Semantic Web technologies are useful
"A little semantics goes a long way"
Some reasoning can be used for consistency checking (Open
Provenance Modelling)
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12. Thanks for your attention! Any question?
A Linked Knowledge Base for Simulation Learning
Paper Authors: Irene Celino, Daniele Dell'Aglio
Contact: Irene Celino – Semantic Web Practice
CEFRIEL – ICT Institute, Politecnico di Milano
email: irene.celino@cefriel.it – web: http://swa.cefriel.it
personal website: http://iricelino.org
phone: +39-02-23954266 – fax: +39-02-23954466
slides available at: http://www.slideshare.net/iricelino
Linked Learning Workshop at ESWC 2011 – Heraklion, Greece – May 29th 2011