Opponent Modeling in Automated Bilateral Negotiation

Can Machine Learning Techniques Outperform State-of-the-Art Heuristic Techniques?

Bachelor thesis (2022)

Authors

T.O. Pocola Electrical Engineering, Mathematics and Computer Science

Contributors

P.K. Murukannaiah Interactive Intelligence - EEMCS (supervisor 1)
B.M. Renting Interactive Intelligence - EEMCS (supervisor 1)
X. Zhang Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Octav Pocola
More Info
expand_more

Published Date

23-06-2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.