Jupiter: An Automated Negotiation Environment for Supporting Agents that Use Machine Learning

The purpose of this paper is to introduce Jupiter, a new environment for automated negotiation in which we can easily create agents that are able to use machine learning in order to extend their knowledge. Automated negotiation is one of the important solutions to coordinate amongst rational agents with conflicting interests. In the literature, previous efforts have been made in order to develop a platform that is able to simulate automated negotiations, i.e., Genius. In this regard, Genius provides an environment for automated negotiation that aims to solve multi-issue negotiation problems. In recent years, with the advancement of hardware technology, the development of machine learning algorithms has seen a remarkable growth. However, there is still few research that provides support for developing new negotiation environments, especially these environments that are able to support machine learning algorithms. In this paper, we propose Jupiter, a new automated negotiation environment in which we can easily create agents that are able to use machine learning in order to extend their knowledge. In addition, we compare Jupiter with Genius and show the performance gains of using Jupiter.