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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. Educational software in the sciences is including open-ended “construction” environments

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Plan Recognition in Virtual Laboratories

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  1. Plan Recognition in Virtual Laboratories Ofra Amir and Ya’akov (Kobi) Gal Ben-Gurion University of The Negev Department of Information Systems Engineering

  2. Background • Educational software in the sciences is including open-ended “construction” environments • Students build and analyze models using these software

  3. Virtual Labs • An interactive simulation of a chemistry lab.

  4. 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. ”

  5. The Problem During class: Please prepare a flask containing 500 ml of a 3M solution… I’m confused Boring… This is easy!

  6. The Problem I wonder how each student performed… After class:

  7. The Goal • Develop automatic support for teachers in their analysis of student performance • Approach: use plan recognition to infer students’ problem solving plans

  8. 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]

  9. Solution Building blocks • User actions • Recipes • Plans

  10. User Actions – Log Files

  11. 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

  12. 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

  13. 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

  14. Plan Recognition Approach Plan Recognition Algorithm Student Plan

  15. 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

  16. Dilution Problem - Example Solution 1 2 1

  17. Dilution Problem - Example Solution 2

  18. Challenges in The Virtual Labs Domain • Indefinite repetition of activities • Interleaving activities • Trial-and-error, mistakes • Conclusion: • Complete approach intractable for this domain

  19. 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

  20. 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

  21. 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

  22. Dilution Problem – Partial Plan MSC – Mixing solution component MSI – Mixing solution through intermediate flask SDP – Solve Dilution Problem

  23. 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

  24. 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

  25. Questions

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