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Published byRuth Nelson Modified over 6 years ago
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Human-Computer Negotiation in Three-Player Market Settings
Galit Haim, Ya'akov Gal, Bo An and Sarit Kraus
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Interactive Negotiation with Three Players
The SPs Goal: to become the CS’s exclusive provider
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The Contract Game Using the Colored Trails: An Infrastructure for Agent Design, Implementation and Evaluation for Open Environments (Gal et al AIJ 2010) Main parts: negotiation movement Incomplete information Automatically exchange Game ends: The CS reached one of the SPs Did not move for two consecutive rounds
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Negotiation Odd Rounds Even Rounds Accept/Reject????
To which SP to propose??? Which proposal to propose??? Even Rounds
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Movement Only the CS can move Chip with the same square-color
150 points bonus: both the CS and the SPg 5 points: for each chip left Only the CS can move Chip with the same square-color Visible movements Path to path More than one square
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The Challenge: Building an Agent that Can Play One of the Roles with People
Sub-Game Perfect Equilibrium Machine Learning + Human Behavior
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Why not Equilibrium Agents? No Need for Culture Consideration
Nash equilibrium: stable strategies; no agent has an incentive to deviate. Results from the social sciences suggest people do not follow equilibrium strategies: Equilibrium based agents played against people failed. People do not build agents that follow equilibrium strategies. Aumann Kahneman
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Sub-Game-Perfect-Equilibrium Agent
Commitment offer: bind the customer to one of the SP for the duration of the game Example: CS proposes 11 grays for 33 red and 7 purple chips
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Extensive Empirical Study: Israel, U.S.A and China
530 students: Israel: 238 students U.S.A: 149 students China: 143 students Baseline: 3 human players One agent vs 2 human players Lab conditions Instructions in the local language: Hebrew, English and Chinese
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EQ CS Player’s Performance
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EQ SPy Player’s Performance
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Human are Bounded Rational: Do not Reach the Goal
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SPy EQ Agent Improvement
Assumption – When a human player attempt to go to the goal, there is some probability p that he will fail Risk-Averse Agent – With respect to probability failure
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Risk Averse Agent Results
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Conclusions Define and analyze a complex three-players negotiation game successfully Equilibrium agents can work well: CS EQ agent outperformed the CS human player SP EQ agent outperformed the SP human player (using the probability to fail)
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Thank you
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