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WISCONSIN UNIVERSITY OF WISCONSIN - MADISON Integrating Knowledge Capture and Supervised Learning through a Human-Computer Interface Trevor Walker, Gautam.

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Presentation on theme: "WISCONSIN UNIVERSITY OF WISCONSIN - MADISON Integrating Knowledge Capture and Supervised Learning through a Human-Computer Interface Trevor Walker, Gautam."— Presentation transcript:

1 WISCONSIN UNIVERSITY OF WISCONSIN - MADISON Integrating Knowledge Capture and Supervised Learning through a Human-Computer Interface Trevor Walker, Gautam Kunapuli, Noah Larsen, David Page, Jude Shavlik KCAP 2011 University of Wisconsin – Madison, WI USA

2 Learning with Domain Knowledge Domain-expert knowledge LearnerModel += Approach: Advice-taking Some learning algorithms can take advice

3 Providing Domain Knowledge is Hard Knowledge Learner + Model = AI/ML expert required Limits applicability of such algorithms

4 Domain Knowledge Difficulties Learner + Model = Domain-expert knowledge

5 Providing Domain Knowledge is Hard Knowledge Want to provide method for non-expert to provide advice Use concrete advice provided via an HCI Learner

6 Notes Want to provide method for non-expert to provide knowledge -> use concrete advice + HCI

7 Domain Knowledge Difficulties 1.Identifying important knowledge is difficult 2.Articulating knowledge requires understanding of underlying algorithm – requires AI expert Learner ? ? Domain-expert knowledge

8 Domain Knowledge Solutions 1.Iteratively acquire knowledge 2.Use advice-taking Human-Computer Interface (HCI) Learner Domain-expert knowledge

9 Learning Scenario Specifics Supervised Learning – Training examples & labels are given – Relational domains Knowledge in the form of advice – Hints to learner, possibly incorrect – Written in first-order logic – Grounded in a specific example

10 Advice Processing [Walker et. al., 2010] train1 car1 Positive Example: eastbound (train1) giraffe Advice: train(train1), has_car(train1, car1), cargo(car1, giraffe) Generated knowledge: bk1(Train) ← train(Train), has_car(Train, Car), cargo(Car, giraffe) Advice: train(train1), has_car(train1, car1), cargo(car1, giraffe) Generated knowledge: bk1(Train) ← train(Train), has_car(Train, Car), cargo(Car, giraffe) Bad: First-Order Logic Good: Grounded Advice

11 Grounded Advice Easier for domain-expert to specify Easier to design HCI for grounded advice

12 Case Study: WillTowerFall? Our HCI advice- taking case study WillTowerFall? – Subset of Wargus

13 WillTowerFall? Sample Scenario

14 Domain Knowledge Solutions 1.Iteratively acquire knowledge 2.Use advice-taking Human-Computer Interface (HCI) Knowledge Learner

15 Our Knowledge Acquisition Loop Domain-expert knowledge Advice HCI Review HCI ModelLearner

16 Giving Advice via the HCI User selects example to provide advice for HCI presents example information User specifies advice by 1. Selecting objects 2. Specifying relations

17 Giving Advice via the HCI Wargus GUI

18 Giving Advice via the HCI Wargus GUI

19 Giving Advice via the HCI Wargus GUI

20 Giving Advice via the HCI

21 Wargus GUI

22 Giving Advice via the HCI Wargus Prototype GUI Make Big

23 Iterative Knowledge Acquisition Advice HCI Review HCI ModelLearner Domain-expert knowledge

24 Reviewing / Correcting Results

25 What Would Users Like to Say? 1.First pass – collect advice in natural language 2.Second pass – provide GUI that supports most of natural language sentences

26 Wargus Natural Language Advice Tower falls Three or more footmen can take down a tower if there is no moat. Five or more archers can take down a tower. Tower stands (does not fall?) If there are only peasants, the tower stands. Four archers or less cannot take down a tower. ANIMATE Percentage Representable87%86% MENTION:

27 Wargus Natural Language Advice Tower will fall – Three or more footmen can take down a tower if there is no moat – Five or more archers can take down a tower Tower will not fall – If there are only peasants, the tower stands – Four archers or less cannot take down a tower 90% representable by GUI

28 Experiments Wargus WillTowerFall? Use boosted relational dependency network (bRDN) – Probabilistic relational supervised learner [Natarajan et. al., 2010] Advice generalization algorithm [Walker et. al., 2010]

29 Experimental Comparisons No Advice User-provide advice via HCI Hand-written AI expert advice

30 Representable Natural Language Language Representable through GUI Feature Mentioned In Advice Context Tower standsTower falls Total number of attackers5036 Number of Archers4362 Number of Footmen5046 Number of Ballistae181 Number of Peasants024 Presence of Moat628 Sum6797 Language NOT Representable through GUI Other (terrain/distance/angle)1016 Percentage Representable87%86%

31 Testset Results GUI-provided advice is equally effective as AI-expert advice

32 Conclusion Determining important knowledge is difficult Iteratively acquire knowledge Articulating knowledge requires understanding of underlying algorithm Use advice-taking Human-Computer Interface ? ?

33 Conclusion Algorithms exist that take advice – However, advice must be written in formal languages – High barrier to entry for non-AI users Designed a GUI – Motivated by natural language statements – Allowed input of grounded advice about specific examples Successful pilot study

34 Future Work Apply framework to additional, more complex domains Develop domain- independent GUI for relational advice taking

35 ACKNOWLEDGEMENTS Thank You DARPA Bootstrap Learning program, grant HR0011-07-C-006

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