Never Before Seen Negotiation Problem

Research on automated negotiation is tackling the challenge of creating agents capable of effective bargaining in diverse, unseen scenarios. Current efforts focus on developing robust learning-based approaches, such as reinforcement learning with graph neural networks and contextual combinatorial bandit algorithms, to handle the complexity of large action spaces and partial observations. These methods aim to improve negotiation outcomes by learning optimal bidding strategies, even with limited information or complex reward structures. This work has implications for both advancing artificial intelligence and creating more efficient and effective negotiation tools in various real-world applications.