Training a Negotiating Agent through Self-Play

Recent developments in applying reinforcement learning to cooperative environments, like negotiation, have brought forward an important question: how well can a negotiating agent be trained through self-play? Previous research has seen successful application of self-play to other settings, like the games of chess and Go. This paper explores the...

Learning the Problem Representation for Improving Negotiation Strategies

The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...

Opponent Modeling in Automated Bilateral Negotiation: Can Machine Learning Techniques Outperform State-of-the-Art Heuristic Techniques?

Automated negotiation agents can highly benefit from learning their opponent’s preferences. Multiple algorithms have been developed with the two main categories being: heuristic techniques and machine learning techniques. Historically, heuristic techniques have dominated the field, but with the recent development in the field of machine learning...

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...