plan recognition in virtual laboratories
Download
Skip this Video
Download Presentation
Plan Recognition in Virtual Laboratories

Loading in 2 Seconds...

play fullscreen
1 / 25

Plan Recognition in Virtual Laboratories - PowerPoint PPT Presentation


  • 72 Views
  • Uploaded on

Plan Recognition in Virtual Laboratories. Ofra Amir and Ya’akov ( Kobi ) Gal Ben-Gurion University of The Negev Department of Information Systems Engineering. Background. Educational software in the sciences is including open-ended “construction” environments

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Plan Recognition in Virtual Laboratories' - early


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
plan recognition in virtual laboratories

Plan Recognition in Virtual Laboratories

Ofra Amir and Ya’akov (Kobi) Gal

Ben-Gurion University of The Negev

Department of Information Systems Engineering

background
Background
  • Educational software in the sciences is including open-ended “construction” environments
  • Students build and analyze models using these software
virtual labs
Virtual Labs
  • An interactive simulation of a chemistry lab.
virtual labs dilution problem
Virtual Labs – Dilution Problem

“ Your supervisor has just asked you to prepare 500mL of 3M HNO3 for tomorrow's experiment. In the stockroom explorer, you will find a cabinet called Stock Solutions. Open this cabinet to find a 2.5L bottle labeled 15.4M HNO3. ”

the problem
The Problem

During class:

Please prepare a flask containing 500 ml of a 3M solution…

I’m confused

Boring…

This is easy!

the problem1
The Problem

I wonder how each student performed…

After class:

the goal
The Goal
  • Develop automatic support for teachers in their analysis of student performance
    • Approach: use plan recognition to infer students’ problem solving plans
related work
Related Work
  • Bayesian networks to model students’ interactions with intelligent tutors [Conati et al., 2002]
  • Complete algorithms for plan recognition (CSP)

[Reddy et al, 2009; Quilici et al, 1998]

  • Heuristic algorithm for recognizing students’ activities in pedagogical software for statistics [Gal et al, 2008]
solution building blocks
Solution Building blocks
  • User actions
  • Recipes
  • Plans
recipes pollack 1990
Recipes [Pollack, 1990]
  • Basic actions are rudimentary (log actions)
  • Complex actions are abstract
  • A recipe for a complex action describes
    • A series of sub-actions for completing the action
    • Constraints on these actions
the recipe language
The Recipe Language
  • Representing actions:
  • Recipe structure:

Constraints on sub-actions parameters

D[dt:H20, sid_1,did_1][ 0 ]

Action Name

An action parameter with value constraint

Action Pre-conditions

Parameter without value constraint

D[sid_1,did_2] -> D[sid_1,did_1][ ] C[sid_2,did_2][ ]

The complex action

sub-actions

did_1=sid_2

plans in virtual labs
Plans in Virtual Labs
  • A plan for a complex action is
    • A hierarchy of recipes towards completing the action
  • The plan represents students’ activity with the software
  • Plan recognition
    • Infer students’ activities based on their actions with the software, given a set of recipes
plan recognition approach
Plan Recognition Approach

Plan Recognition

Algorithm

Student Plan

dilution problem recipes
Dilution Problem Recipes
  • To solve the problem, the student should:
    • Pour H20 to the destination flask
    • Pour HNO3 to the destination flask
  • Students can solve this problem in many different ways
challenges in the virtual labs domain
Challenges in The Virtual Labs Domain
  • Indefinite repetition of activities
  • Interleaving activities
  • Trial-and-error, mistakes
  • Conclusion:
    • Complete approach intractable for this domain
build plan greedy algorithm
Build Plan Greedy Algorithm
  • Input:
    • A set of Virtual Labs basic actions, A set of recipes
  • Algorithm steps:
    • initialize open list with the actions from the log
    • for each recipe in order of increasing depth
      • find match (Recipe, open list)
      • while match exists
        • add complex action to the open list
        • create branches from the complex action to its sub-actions
        • remove sub-actions from open list
        • Call find match with the updated open list
slide20
Recipe: C -> SM SM

Recipe: D -> C C

( SM , did=ID5 , sid=ID1 )

( SM , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( SM , did=ID6 , sid=ID5 )

( MO , id=ID1 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( SM , did=ID6 , sid=ID5 )

( C , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( C , did=ID6 , sid=ID5 )

( C , did=ID5 , sid=ID1 )

( MO , id=ID5 )

( MO , id=ID5 )

( AS , id=ID6 )

( MO , id=ID6 )

( MO , id=ID5 )

( FC , did=ID6 ,sid=ID5 )

( C , did=ID6 , sid=ID5 )

( D , did=ID6 , sid=ID1 )

P3

P2

P1

find match
Find Match
  • Find match searches for actions in the open list which fulfill the recipe for the complex action
  • Actions in the match can be free ordered as long as they satisfy the constraints in the recipe
  • Was implemented as depth first search, but can be implemented in other ways
  • Find Match is complete, given a recipe and an open list of actions
dilution problem partial plan
Dilution Problem – Partial Plan

MSC – Mixing solution component

MSI – Mixing solution through intermediate flask

SDP – Solve Dilution Problem

empirical evaluation
Empirical Evaluation
  • The algorithm was run on 20 log files taken from real student interactions
    • 6 different problems
    • logs ranged in size from 20 actions to 187 actions
    • Plans ranged in depth from 3 to 14 levels
  • The plans were validated by a domain expert
contributions
Contributions
  • A new computationally efficient plan recognition algorithm that can cope with interleaving activities, mistakes, indefinite repetition.
  • The algorithm can be integrated with real pedagogical software
  • Shown to succeed on real-world data
ad