Automated Negotiation
Overview
Automated negotiation studies how software agents can reach agreements when parties have different goals, limited information, or competing interests. Approaches range from game theory, Bayesian opponent modeling, and reinforcement learning to language-model agents that negotiate through dialogue, sometimes with human advisors or mediators. Researchers test these methods in settings such as buyer–seller markets, multi-party bargaining, contracts, and resource allocation. A key challenge is that agreement rates alone can hide poor outcomes: agents must also be assessed for surplus, fairness, efficiency, reliability, and safeguards such as privacy and consent.
Latest papers
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