Opponent Modeling in Automated Bilateral Negotiation

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

Pocola, Octav (TU Delft Electrical Engineering, Mathematics and Computer Science)

Murukannaiah, P.K. (mentor)
Renting, B.M. (mentor)
Zhang, X. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-23

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.

Automate negotiation
Bilateral negotiation
Multi-issue negotiation
Opponent modeling

http://resolver.tudelft.nl/uuid:4662d084-2a09-4b49-a58b-4d40f38a8e0e

Student theses

bachelor thesis

© 2022 Octav Pocola