Multilateral Multi-issue Automated Negotiation Model Based on GA

Author(s):  
Li Liu ◽  
Yan Ma ◽  
Cuimei Wen ◽  
Lian Li
2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Pallavi Bagga ◽  
Nicola Paoletti ◽  
Bedour Alrayes ◽  
Kostas Stathis

AbstractWe present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.


2013 ◽  
Vol 8 (10) ◽  
Author(s):  
Hongwei Chen ◽  
Shuping Wang ◽  
Hui Xu ◽  
Zhiwei Ye ◽  
Chunzhi Wang

2021 ◽  
Vol IX(253) (45) ◽  
pp. 66-69
Author(s):  
R. Baghramyan

This article focuses on institutional discourse pragmatics, highlighting its salient features in a business to customer negotiation model, based on an authentic business correspondence (email), through a prism of H. Grice’s Theory of Cooperative Principle. The central thrust of this theory is the application of the maxims of Quantity, Quality, Relation and Manner to speech acts to secure identification of participants’ intentions and sentence meaning, excluding irrelevancies throughout the communication process. Any kind of interaction in our life is accompanied by diverse tangible and intangible components. The role, function and impact of these aggregate components on the natural flow of communication irrespective of the form (oral/written) are the concern of pragmatics.


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