Skip to main content
WorldCIST'19 - 7th World Conference on Information Systems and Technologies

Full Program »

Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learning

This paper proposes an adaptation of the Q-Learning reinforcement learning algorithm, for the identi fication of the most probable scenario that a player may face, under different contexts, when negotiating bilateral contracts. For that purpose, the proposed methodology is integrated in a Decision Support System that is capable to generate several different scenarios for each negotiation context. With this complement, the tool can also identify the most probable scenario for the identifi ed negotiation context. A realistic case study is conducted, based on real contracts data, which con cerns the learning capabilities of the proposed methodology. It is possible to identify the most probable scenario for each context over the learned period. Nonetheless, the identifi ed scenario might not always be the real negotiation scenario, given the variable nature of such negotiations. However, this work greatly reduces the frequency of such unexpected scenarios, contributing to a greater success of the supported player over time.

Francisco Silva
GECAD/IPP

Tiago Pinto
University of Salamanca
Portugal

Isabel Pra├ža
Polytechnic of Porto
Portugal

Zita Vale
Polytechnic of Porto
Portugal

 


Powered by OpenConf®
Copyright ©2002-2018 Zakon Group LLC