An efficient Bayesian learning-based opponent model considering parametric interrelation in automated bilateral multi-issue negotiation

Key Points

• Negotiation outcomes improve with the introduction of a Bayesian learning model, streamlining decisions in automated settings.
• The model leverages parametric interrelation to better predict opponents’ strategies across multiple issues.
• Analysis involves multi-issue negotiation scenarios, demonstrating the model's effectiveness in complex decision environments.
• Overall, this study highlights the importance of advanced modeling techniques in optimizing automated negotiation processes.