Learning a Latent Representation of the Opponent in Automated Negotiation

This paper introduces a strategy for learning opponent parameters in automated negotiation and using them for future negotiation sessions. The goal is to maximize the agent’s utility while being consistent in its performance over various negotiation scenarios. While a number of reinforcement learning approaches in the field have used Q-learning,...

Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequence of Offers

With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...