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Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games.

Dor Mizrahi1, Inon Zuckerman1,2, Ilan Laufer1

  • 1Department of Industrial Engineering and Management, Ariel University, Ariel 40700, Israel.

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This study developed an intelligent agent for human-machine collaboration that uses Social Value Orientation (SVO) to optimize cooperation in tacit coordination games. The agent outperformed human players by strategically choosing between greedy and cooperative policies.

Keywords:
autonomous agentcognitive modelingdecision-makingdivergent interestsocial value orientation (SVO)tacit coordination

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Area of Science:

  • Robotics and Artificial Intelligence
  • Behavioral Game Theory
  • Human-Machine Interaction

Background:

  • Collaborative robots are key in Industry 5.0, necessitating improved human-machine collaboration algorithms.
  • Efficient collaboration requires computational strategies that minimize processing and communication costs.
  • Tacit coordination games with divergent interests and no communication present unique challenges for autonomous agents.

Purpose of the Study:

  • To construct an intelligent agent capable of optimal cooperation strategy selection in tacit coordination games.
  • To incorporate a behavioral model predicting player convergence on focal points based on Social Value Orientation (SVO).
  • To enhance agent utility maximization in scenarios with asymmetric payoffs and no communication.

Main Methods:

  • Developed an agent based on a behavioral model predicting focal point convergence using SVO and game features.
  • Implemented a stochastic policy selection between greedy and cooperative strategies.
  • Utilized Social Value Orientation (SVO) theory to inform decision-making in resource allocation contexts.

Main Results:

  • The agent incorporating SVO achieved a higher mean score distribution compared to human players competing against each other.
  • The agent's performance surpassed that of agents using only a greedy or only a focal point strategy.
  • Demonstrated superior reward maximization through SVO-informed strategy selection in tacit bargaining.

Conclusions:

  • This research presents the first intelligent agent maximizing utility by integrating player belief systems (SVO) in tacit bargaining.
  • The SVO-based reward-maximizing strategy selection offers potential applications in various human-machine contexts and multiagent systems.
  • The developed agent enhances human-machine collaboration by optimizing cooperation in complex game-theoretic scenarios.