BDI-based Opponent Modeling and Strategy Generation for Multi-Issue Negotiation (Student Abstract)

Abstract

Accurately modeling opponent behaviors and integrating strategy are key challenges for multi-issue automated negotiation. Existing approaches often isolate preference learning or trend prediction and lack a unified cognitive structure with coordinated reasoning. This paper proposes a BDI (Belief-Desire-Intention)-based opponent modeling and strategy generation framework. The framework analyzes opponent responses (Belief), predicts preference weights and the utility function (Desire), and infers utilities of future offers (Intention). Building on these predictions, we design a responsive strategy, enabling gradual concessions and balanced outcomes. Our main contributions are: D-MBUE in the Desire module, I-DABI in the Intention module, and the BDI Negotiator on top of the modeling modules. Experiments on 45 standard negotiation domains and against 12 representative opponents demonstrate the effectiveness of our BDI framework.