1. Asking Questions and Developing Trust Stephanie Rosenthal Joint Work with Anind K. Dey and Manuela Veloso Carnegie Mellon University

2. Stephanie RosenthalAsking Questions and Developing Trust2 Overview Agent/ Robot Human(s) Questions Responses

3. Stephanie RosenthalAsking Questions and Developing Trust3 Agents Asking Questions Robot Email Sorter Agent should explicitly share its state information –context, prediction, uncertainty, features Goal: Vary which information the agent shares to maximize accuracy

4. Stephanie RosenthalAsking Questions and Developing Trust4 Questions - Robot Example Robot State –Context - image of the structure, color, position –Prediction - shape of block –Uncertainty - probability the prediction is wrong –Features - context that defines the block

5. Stephanie RosenthalAsking Questions and Developing Trust5 Questions - Robot Example Robot State –Context - image of the structure, color, position –Prediction - shape of block –Uncertainty - prob. prediction is wrong –Features - context that defines the block Example Question –Cannot determine the block shape. You are working with the red and purple blocks. What shape is the red block? I think it is a triangular prism.

6. Stephanie RosenthalAsking Questions and Developing Trust6 Questions - Robot Example Robot State –Context - image of the structure, color, position –Prediction - shape of block –Uncertainty - prob. prediction is wrong –Features - context that defines the block Example Questions –You are working with the red and purple blocks. What shape is the red block? What features define that shape?

7. Stephanie RosenthalAsking Questions and Developing Trust7 Findings Robot Email

8. Stephanie RosenthalAsking Questions and Developing Trust8 Overview Agent/ Robot Human(s) Questions Responses Agents can manipulate their questions to maximize the accuracy of human responses

9. Stephanie RosenthalAsking Questions and Developing Trust9 Recommender System Goal: Provide personal predictions for each user N products p 1 p 2 … p N r 1 r 2 … r M M reviewers R ij R reviews c 1 c 2 c K Categories

10. Stephanie RosenthalAsking Questions and Developing Trust10 Advice Givers Domain-Independent Initialize w i u = 1/M For all products p j that user u requests predictions for: –Make prediction argmax v I(R ij ==v)  w u –If user gives opinion o j Update weights w u Domain-Specific Initialize w i u,d = 1/M For all products p j that user u requests predictions for: –d = domain of p j –Make prediction argmax v I(R ij ==v)  w u,d –If user gives opinion o j Update weights w u,d Good: Reduces sparsity Bad: Single set of weights Good: Category-based weights Bad: Less data in each category

11. Stephanie RosenthalAsking Questions and Developing Trust11 Which Advice Giver is Better? Tradeoff between data and precision is not uniform across users User-dependent selection algorithm to decide which advice giver is best for each user DIDSSelection

12. Stephanie RosenthalAsking Questions and Developing Trust12 Summary Agent/ Robot Human(s) Questions Responses Genre and frequency of questions affects the way that the agent should develop trust with reviewers Agents can manipulate their questions to maximize the accuracy of human responses

13. Stephanie RosenthalAsking Questions and Developing Trust13 Future Work Agent/ Robot Human(s) Questions Responses Genre and frequency of questions affects the way that the agent should develop trust with reviewers *with Mike Licitra, Nick Armstrong-Crews, Joydeep Biswas Agents can manipulate their questions to maximize the accuracy of human responses

14. Stephanie RosenthalAsking Questions and Developing Trust14 Questions?

15. Stephanie RosenthalAsking Questions and Developing Trust15

16. Stephanie RosenthalAsking Questions and Developing Trust16 Recommender System Advice giver weighs each reviewer for each user w i u –For all users, initialize w i u = 1/M –When user u provides an actual opinion o j about a product p j, update all weights w u w i u = e^(ln(w i u ) - |R ij - o j |)/K Advice giver predicts value v for product p j and user uAdvice giver predicts value v for product p j and user u –argmax v I(R ij ==v)  w u

17. Stephanie RosenthalAsking Questions and Developing Trust17 Advice Giver Algorithm Initialize w i u = 1/M For all products p j that user u requests predictions for: –Make prediction argmax v I(R ij ==v)  w u –If user gives opinion o j Update weights w u

18. Stephanie RosenthalAsking Questions and Developing Trust18 Category-Dependent Advice Giver Algorithm Initialize w i u,k = 1/M For all products p j that user u requests predictions for: –k = category of p j –Make prediction argmax v I(R ij ==v)  w u,k –If user gives opinion o j Update weights w u,k only

19. Stephanie RosenthalAsking Questions and Developing Trust19 Tradeoffs Category-Independent Advice Giver –More data to evaluate the weights of each reviewer, coarser trust model Category-Dependent Advice Giver –More fine-grained evaluation of which reviewers to trust, less data per category Amazon.com, Netflix.com, Yahoo! Music –V = {1,2,3,4,5}, M > 100K, N > 50 per user –C = ~10 per dataset, 20 test users per dataset

20. Stephanie RosenthalAsking Questions and Developing Trust20 Overview Asking Questions of Novice Users Developing Trust in Large Sets of Online Users CoBot the Visitor Companion Robot

21. Stephanie RosenthalAsking Questions and Developing Trust21 Asking Questions Agent should explicitly share its state information –context, prediction, uncertainty, features Goal: Vary which information the agent shares to maximize accuracy

22. Stephanie RosenthalAsking Questions and Developing Trust22 Developing Trust in Humans Case Study - Recommender Systems

23. Stephanie RosenthalAsking Questions and Developing Trust23 CoBot, Visitor Companion Escort a human visitor to their meetings Navigate indoor environments Share information relevant to the meetings Ask questions when it cannot perform a task