Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Published byHoratio Goodwin Modified over 8 years ago

Similar presentations

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

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

Similar presentations

Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California

Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California
Attributes for Classifier Feedback Amar Parkash and Devi Parikh.

Attributes for Classifier Feedback Amar Parkash and Devi Parikh.
Alina Pommeranz, MSc in Interactive System Engineering supervised by Dr. ir. Pascal Wiggers and Prof. Dr. Catholijn M. Jonker.

Alina Pommeranz, MSc in Interactive System Engineering supervised by Dr. ir. Pascal Wiggers and Prof. Dr. Catholijn M. Jonker.
4 Intelligent Systems.

4 Intelligent Systems.
1 Unsupervised Semantic Parsing Hoifung Poon and Pedro Domingos EMNLP 2009 Best Paper Award Speaker: Hao Xiong.

1 Unsupervised Semantic Parsing Hoifung Poon and Pedro Domingos EMNLP 2009 Best Paper Award Speaker: Hao Xiong.
Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle Irwin King and Zhong Jin Nov. 2001.

Relevance Feedback Content-Based Image Retrieval Using Query Distribution Estimation Based on Maximum Entropy Principle Irwin King and Zhong Jin Nov
LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.

LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.
Algorithms and Problem Solving-1 Algorithms and Problem Solving.

Algorithms and Problem Solving-1 Algorithms and Problem Solving.
MITRE © 2001 The MITRE Corporation. ALL RIGHTS RESERVED. What Works, What Doesn’t -- And What Needs to Work Lynette Hirschman Information Technology Center.

MITRE © 2001 The MITRE Corporation. ALL RIGHTS RESERVED. What Works, What Doesn’t -- And What Needs to Work Lynette Hirschman Information Technology Center.
Algorithms and Problem Solving. Learn about problem solving skills Explore the algorithmic approach for problem solving Learn about algorithm development.

Algorithms and Problem Solving. Learn about problem solving skills Explore the algorithmic approach for problem solving Learn about algorithm development.
LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.

LEARNING FROM OBSERVATIONS Yılmaz KILIÇASLAN. Definition Learning takes place as the agent observes its interactions with the world and its own decision-making.
Guided Conversational Agents and Knowledge Trees for Natural Language Interfaces to Relational Databases Mr. Majdi Owda, Dr. Zuhair Bandar, Dr. Keeley.

Guided Conversational Agents and Knowledge Trees for Natural Language Interfaces to Relational Databases Mr. Majdi Owda, Dr. Zuhair Bandar, Dr. Keeley.
Overview of Long-Term Memory laura leventhal. Reference Chapter 14 Chapter 14.

Overview of Long-Term Memory laura leventhal. Reference Chapter 14 Chapter 14.
Latent Semantic Analysis (LSA). Introduction to LSA Learning Model Uses Singular Value Decomposition (SVD) to simulate human learning of word and passage.

Latent Semantic Analysis (LSA). Introduction to LSA Learning Model Uses Singular Value Decomposition (SVD) to simulate human learning of word and passage.
RL via Practice and Critique Advice Kshitij Judah, Saikat Roy, Alan Fern and Tom Dietterich PROBLEM: RL takes a long time to learn a good policy. Teacher.

RL via Practice and Critique Advice Kshitij Judah, Saikat Roy, Alan Fern and Tom Dietterich PROBLEM: RL takes a long time to learn a good policy. Teacher.
Prototype & Design Computer Inputs. How to Prototype & Design Computer Inputs Step 1: Review Input Requirements Step 2: Select the GUI Controls Step 3:

Prototype & Design Computer Inputs. How to Prototype & Design Computer Inputs Step 1: Review Input Requirements Step 2: Select the GUI Controls Step 3:
Machine Learning CS 165B Spring 2012

Machine Learning CS 165B Spring 2012
Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.

Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.
Issues with Data Mining

Issues with Data Mining
DOPROPC: a domain property pattern system helping to specify control system requirements Fan WuHehua ZhangMing Gu School of Software, Tsinghua University.

DOPROPC: a domain property pattern system helping to specify control system requirements Fan WuHehua ZhangMing Gu School of Software, Tsinghua University.

Similar presentations

Ads by Google