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, this is no longer true. The main goal of the paper is to compare these two techniques quantitatively using the Pearson correlation of bids. The models that were chosen as the heuristic and machine learning baseline are the Smith and the Perceptron models, respectively. Our results show that the two baselines have similar performance. This leads us to conclude that machine learning algorithms have caught up with their heuristic counterparts. Furthermore, we have also found a statistically significant correlation between the Perceptron model’s accuracy and the seen bid space.CSE3000 Research ProjectComputer Science and Engineerin