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- 1. Ridesharing from the simple matching scenario to the real world application Sofia Ceppi Michael Rovatsos Pavlos Andreadis
- 2. Andy Betty Charles Diana Edward
- 3. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 4. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 5. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 6. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 7. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 8. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
- 9. Uncertainty due to human behaviour
- 10. Uncertainty due to human behaviour imposing a solution recommending a set of solutions
- 11. Recommending a set of solutions: Problems
- 12. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
- 13. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
- 14. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 15. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 16. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 17. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 18. Single User and Collective of Users
- 19. Single User and Collective of Users User's utility function: - how good a solution is for the user - depends on user's requirements and preferences System's utility function: - depends on social welfare - other factors
- 20. Single User and Collective of Users User's utility function: - how good a solution is for the user - depends on user's requirements and preferences System's utility function: - depends on social welfare - other factors
- 21. Set of Solutions Influence the Users Learning
- 22. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning
- 23. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
- 24. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
- 25. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints feasibility
- 26. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
- 27. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
- 28. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints feasibility MIP*
- 29. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
- 30. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
- 31. Set of Solutions Influence the Users Learning
- 32. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 33. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 34. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 35. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 36. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 37. Set of Solutions Influence the Users Learning
- 38. Set of Solutions Influence the Users Learning 1) utility model: what users like 2) response model: how users behave 3) belief update: done after each user's action
- 39. Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 40. PROBLEM: properties guaranteed by the MIPs may not hold when taxation is applied Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 41. MISSING: active learning PROBLEM: properties guaranteed by the MIPs may not hold when taxation is applied Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 42. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 43. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 44. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 45. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 46. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 47. MIPs + Taxation MIPfirst MIPothers MIP*
- 48. MIPs + Taxation MIPfirst MIPothers MIP* Sponsored Solution
- 49. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective ConstraintsSponsored Solution
- 50. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Sponsored Solution
- 51. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 52. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 53. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 54. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 55. MIPs + Taxation + Active Learning
- 56. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPs + Taxation + Active Learning
- 57. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPothers MIPs + Taxation + Active Learning
- 58. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPothers “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) max EVOI → max Expected Utility MIPs + Taxation + Active Learning
- 59. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option MIPs + Taxation + Active Learning
- 60. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: commend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system MIPs + Taxation + Active Learning
- 61. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work commend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results the result holds also in this case MIPs + Taxation + Active Learning
- 62. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work ● recommend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results the result holds also in this case MIPs + Taxation + Active Learning
- 63. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work ● recommend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results same results the result holds also in this case MIPs + Taxation + Active Learning
- 64. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommend recommendation set + influence users + learning Thank you - Questions?
- 65. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning Thank you - Questions?
- 66. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning
- 67. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning

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