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Prediction of Nash Bargaining Solution in Negotiation Dialogue [PRICAI '18]

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Prediction of Nash Bargaining Solution in Negotiation Dialogue [PRICAI '18]

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Prediction of Nash Bargaining Solution in Negotiation Dialogue [PRICAI '18]

  1. 1. Prediction of Nash Bargaining Solution in Negotiation Dialogue Kosui Iwasa, Katsuhide Fujita Tokyo University of Agriculture and Technology
  2. 2. Outline Background Related Works Problem Definition Proposed Method Evaluation Conclusion
  3. 3. Background
  4. 4. Importance of Negotiations Generally, there are negotiations in … business politics judicature Negotiations are essential activities for people.
  5. 5. A Problem in Negotiations Solutions Pareto Front Nash Bargaining Solution Reward for !" Rewardfor!# com prom ise Actual Agreement
  6. 6. Main Purpose of our Study Utility for !" Utilityfor!# Solving the gap between Nash bargaining solutions and actual agreements Nash Bargaining Solution Actual Agreement Predicting Nash Bargaining Solution from negotiation dialogues
  7. 7. Contributions Proposed Method Predicting Nash bargaining solution by deep learning from dialogues in natural language in multi-issue negotiations Experimental Result In Social welfare and Nash product, solutions predicted by the our method are superior to solutions formed in human-human negotiations
  8. 8. Related Work
  9. 9. Automated Negotiation Expected to support or act on human negotiations. Agents can negotiate with each other to make agreements by the predetermined negotiation protocol (not in natural languages). GENIUS: Automated Negotiation Platform
  10. 10. Comparison between agents and humans in negotiations In simple protocol With well-defined utility functions Agents In natural languages With unclear preferences Humans The negotiation between humans have to ! define their own utility functions ! negotiate in simple protocols (not natural languages)
  11. 11. Human-Human Negotiation dialogues Where should we go on a trip in our next vacation? I want to go to Tokyo and see a famous shrine. Too bad. Prices in Tokyo are too high. How about Beijing? A B C
  12. 12. Agent-Agent Negotiation dialogues OFFER: Tokyo OFFER: Beijing A B OFFER: Shanghai C ACCEPT A
  13. 13. Agent-Agent Negotiations (SAOP) in Multi-issue Negotiation Problems OFFER: Budget - $300 Term- 5 days Destination – Tokyo Transportation – Airplane Hotel – Raymond Hotel Day #1 – Go to Sensou-ji A … It is complicated to humans
  14. 14. The End-to-end Negotiator in a Natural Language Deal or No Deal? End-to-End Learning for Negotiation Dialogues [Lewis et al. 2017]
  15. 15. Solution Generator The End-to-end Negotiator in a Natural Language Input Dialogue Encoder <OTHER> I want hatsto Speech Generator <ME> Is it ? < OTHER > I want ?to Hat: 2, Book: 1, Ball: 0
  16. 16. Negotiation Agents in Natural Languages !They could not outperform the results of humans in both individual utility and social welfare. !To act the agent in the user’s place in the real world, their own utility functions should be defined.
  17. 17. Problem Definition
  18. 18. Summary of the Problem Definition Two participants !", !$ exchange some items Multi-issue negotiation The utility of each agent is calculated as the weighted average of option's score There is no dependency between issues
  19. 19. An Example of Issues and options How to allocate fruitsDomain ApplesIssues Bananas Oranges Options (# of items) 0 2… 0 5… Every issue has options, which is a integer and a limited range 0 3…
  20. 20. An Example of A Solution Domain Issues Options (# of items) One of A Solution 1 2 2 How to allocate fruits 0 2… 0 5… 0 3… Apples Bananas Oranges
  21. 21. How To Calculate Utility For Each Agent? Participant !" Participant !# Weights Apple: 0.5 Banana: 0.3 Orange: 0.1 Weights Apple: 0.1 Banana: 0.2 Orange: 0.7
  22. 22. How To Calculate Utility For Each Agent? Participant !" Participant !# Weights Apple: 0.5 Banana: 0.3 Orange: 0.1 Weights Apple: 0.1 Banana: 0.2 Orange: 0.7
  23. 23. How To Calculate Utility For Each Agent? Participant !" Weights Apple: 0.5 Banana: 0.3 Orange: 0.1 0.5 & 2 2 + 0.3 & 3 5 + 0.1 & 0 3 = 0.68 Utility for !" in the solution Apple Banana Orange
  24. 24. Proposed Method
  25. 25. Outline of the proposed method 1. Predict the weights of each issue for each participant from negotiation dialogues in natural languages 2. Search for Nash bargaining solution through exhaustive search based on the predicted weights
  26. 26. Outline of the proposed method 1. Predict the weights of each issue for each participant from negotiation dialogues in natural languages 2. Search for Nash bargaining solution through exhaustive search based on the predicted weights
  27. 27. 1. Predict the weights of each issue for each participant I. Preprocessing
  28. 28. Input 1. Predict the weights of each issue for each participant II. Prediction with Bi-GRUs Bi-GRUs Encoder Attention Output <TGT> I want <END>to ! Apple: 0.4 Banana: 0.5 Orange: 0.1 Softmax
  29. 29. Summary of the proposed method 1. Predict the weights of each issue for each participant from conversations 2. Search for Nash bargaining solution through exhaustive search based on the predicted weights
  30. 30. Evaluation
  31. 31. Experimental Settings Dataset: provided by Facebook AI research For the end-to-end negotiator (Lewis et al.) Two humans negotiate in English and allocate books, hats, and balls. Hyperparameters The gradient method: RMSProp The number of GRU units: 256
  32. 32. Experiment #1 Prediction of Issue Weights Evaluate the quality of prediction of issue weights 10-fold cross-validation to evaluate Spearman's rank vs Ground truths: 61% In prediction of the rank of item's importance Accuracy: 70% In prediction of the most important item
  33. 33. Experiment #2 Prediction of Nash bargaining solution Evaluate the quality of predicted solutions by comparing with agreements in human-human negotiations Metrics Nash Product The product of utilities in each participant Social Welfare The sum of utilities in each participant
  34. 34. Experiment #2 Result in Nash Product
  35. 35. Experiment #2 Result in Social Welfare
  36. 36. Conclusion
  37. 37. Conclusion Proposed Method Predict Nash bargaining solution from dialogues by natural language in a multi-issue negotiation using Bidirectional GRUs Experimental Results In Social welfare and Nash product, the solutions predicted by our method are superior to the solutions in human-human negotiations

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