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German Research Center for Artificial Intelligence (DFKI GmbH) Saarbrücken, Germany Deutsches Forschungszentrum für Künstliche Intelligenz Course Generation.

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Presentation on theme: "German Research Center for Artificial Intelligence (DFKI GmbH) Saarbrücken, Germany Deutsches Forschungszentrum für Künstliche Intelligenz Course Generation."— Presentation transcript:

1 German Research Center for Artificial Intelligence (DFKI GmbH) Saarbrücken, Germany Deutsches Forschungszentrum für Künstliche Intelligenz Course Generation Based on Hierarchical Task Network (HTN) Planning Carsten Ullrich, ABIS05

2 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Learn about derivation Course Generation, Example Repository Course Generator

3 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 : : Represent advanced pedagogical knowledge Joint work with University of Augsburg, Lehrstuhl für Didaktik der Mathematik Formalization of pedagogical strategies based on OECD PISA –Focus on mathematical literacy & competencies problem solving, use of mathematical language, mathematical modeling … –6 scenarios: LearnNew, Rehearse, Overview, trainCompetency, Workbook, ExamSimulation. Pedagogical Aims

4 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Basics of Hierarchical Task Network Planning Similar to classical AI planning –World state represented by set of atoms –Actions correspond to state transitions Differences to classical AI planning –What it plans for: Sequence of actions that perform a task network –How it plans: Methods decompose tasks down to primitive tasks performed by operators

5 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Basics of HTN-Planning Domain description –Tasks: Activities to perform Primitive/Compound –Operators: Perform primitive tasks (task, precondition, delete list, add list) –Methods: Decompose compound tasks (task, precondition, subtasks) –Axioms: Infer preconditions not asserted in world state Shop2/JShop2: D. Nau et.al., University of Maryland

6 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Shop2 Planning Algorithm Shop2: D. Nau et.al., University of Maryland Ordered task decomposition –Generates plan steps in execution order –Knows current state at each step For each non-primitive task: –Choose applicable method, instantiate, decompose For each primitive task: –Choose applicable operator, instantiate, apply Leaf nodes of decomposition tree: Actions

7 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05Challenges Reasoning about Content Reasoning about User Tool Support Adaptivity++, Interactivity, Service Provision Pedagogical Knowledge Repository Learn about derivation Course Generator Repository ? ? ? ? Examples for derivation ?

8 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Connecting Course Generation and HTN Input: Pedagogical Task Pedagogical Objective List of Content Identifiers (learnNew (def_function def_deriv)) (getAppropriateExercise (def_function)) Output: Actions generating structured sequence of LOs ((!startSection def_derivation) (!insertElement intro_def_funct) (!insertElement def_function) … (!endSection))

9 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Course Generation by HTN, Example (:method (learnConceptLearnNew ?c) MethodLearnConceptLearnNew () ((!startSection LearnNew) (introduce ?c) (developConcept ?c) (practice! ?c) (connect ?c) (reflect ?c) (!endSection))) Goal Task Subtask Preconditions

10 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Course Generation by HTN, Example (:method (learnConceptLearnNew ?c) MethodLearnConceptLearnNew () ((!startSection LearnNew) (introduce ?c) (developConcept ?c) (practice! ?c) (connect ?c) (reflect ?c) (!endSection)))

11 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Decomposing a Task (:method (introduce ?c) MethodIntroduce () ((!startSection (introduce ?c)) (insertMotivation ?c) (introductionExamplify ?c) (learnPrerequisitesConceptsShort ?c) (!endSection))) Optional tasks: Should be achieved, but do not fail (:method (introduce ?c) MethodIntroduceFallback () ())

12 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Critical Tasks Critical tasks: Have to be fulfilled, otherwise fail (:method (learnConceptProblemBased ?c) () ((insertProblem! ?c) (do something …)))

13 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Reasoning about User (:method (introductionExamplify ?c) MethodIntroductionExamplify ((learnerProperty anxiety ?an)(call > ?an 2) (assignIterator ?element (call GetElements ((class example) (property difficulty easy) (relation for ?c))))) ((!insertElement ?element))) (:- (learnerProperty ?property ?value) (same ?value (call queryLM ?property)))

14 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Reasoning about Content (:method (introductionExamplify ?c) MethodIntroductionExamplify ((learnerProperty anxiety ?an)(call > ?an 2) (assignIterator ?element (call GetElements ((class example) (property difficulty easy) (relation for ?c))))) ((!insertElement ?element))) (:- (assignIterator ?element (?head. ?tail)) ((same ?element ?head))) (:- (assignIterator ?element (?head. ?tail)) ((assignIterator ?element ?tail))) (id1 id2 id3 …)

15 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 (:method (learnConceptLearnNew ?c) MethodLearnConceptLearnNew () ((!startSection LearnNew) (introduce ?c) (developConcept ?c) (practice ?c) (connect ?c) (reflect ?c) (!endSection) ) Course Generation by HTN, Example

16 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Crucial and Fallback Methods (:method (developConcept ?c) ((learnerProperty competencyLevel ?c ?cl) (call > ?cl 3)) ((!startSection (develop ?c)) (!insertElement ?c) (!endSection)) ((learnerProperty competencyLevel ?c ?cl) (call <= ?cl 3) (learnerProperty motivation ?c ?mo) (call >= ?mo 3)) ((!startSection (develop ?c)) (!insertElement ?c) (explain ?c) (selectAppropriateExample ?c) (!endSection)) () ((!startSection (develop ?c)) (!insertElement ?c) (explain ?c) (!endSection))) Crucial methods Fallback method

17 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 (:method (learnConceptLearnNew ?c) MethodLearnConceptLearnNew () ((!startSection LearnNew) (introduce ?c) (developConcept ?c) (practice ?c) (connect ?c) (reflect ?c) (!endSection) ) Course Generation by HTN, Example

18 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Selecting Exercises (:method (selectAppropriateExercise ?c) MethodSelectExerciseHighMotivation ((learnerProperty field ?field) (learnerProperty educationalLevel ?el) (learnerProperty motivation ?c ?m) (call >= ?m 3) (learnerProperty competencyLevel ?c ?cl) (equivalent (call + 1 ?cl) ?ex_cl) (assignIterator ?exercise (call GetElements ((class exercise) (relation for ?c) (property learningcontext ?el) (property competencylevel ?ex_cl) (property field ?field))))) ((insertElement ?exercise))) Service Provision!

19 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 (:method (learnConceptLearnNew ?c) MethodLearnConceptLearnNew () ((!startSection LearnNew) (introduce ?c) (developConcept ?c) (practice ?c) (connect ?c) (reflect ?c) (!endSection) ) Course Generation by HTN, Example

20 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05 Service Integration (:method (reflect ?c) MethodReflectWithOLM ((learningServiceAvailable OLM)) ((!startSection reflect) (!insertLearningService OLM) (!endSection)) MethodReflectManual () ((!startSection reflect) (!text reflect) (!endSection))) (:- (learningServiceAvailable ?tool) ((call checkService ?tool)))

21 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05Conclusion Using HTN-planning for Course Generation –Representing pedagogical knowledge –Distributed content, integration of learning services –Adaptivity++, interactivity, service provision, sub-goal recognition Efficient: –Generation of a course with 15 LO 700ms –with caching 200ms

22 German Research Center for Artificial Intelligence Carsten Ullrich – ABIS05Thanks


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