A bilateral negotiation model of technology pricing

Author(s):  
Liping Fang ◽  
Yuanfang Song ◽  
Yingluo Wang
Author(s):  
Amruta More ◽  
Sheetal Vij ◽  
Debajyoti Mukhopadhyay

The research in the area of automated negotiation systems is going on in many universities. This research is mainly focused on making a practically feasible, faster and reliable E-negotiation system. The ongoing work in this area is happening in the laboratories of the universities mainly for training and research purpose. There are number of negotiation systems such as Henry, Kasbaah, Bazaar, Auction Bot, Inspire, Magnet. Our research is based on making an agent software for E-negotiation which will give faster results and also is secure and flexible. Cloud Computing provides security and flexibility to the user data. Using these features we propose an E-negotiation system, in which, all product information and agent details are stored on the cloud. This system proposes three conditions for making successful negotiation. First rule based, where agent will check user requirements with rule based data. Second case based, where an agent will see case based data to check any similar previous negotiation case is matching to the user requirement. Third bilateral negotiation model, if both rules based data and case based data are not matching with the user requirement, then agent use bilateral negotiation model for negotiation. After completing negotiation process, agents give feedback to the user about whether negotiation is successful or not. Using rule based reasoning and case based reasoning this system will improve the efficiency and success rate of the negotiation process.


2020 ◽  
Vol 11 (4) ◽  
pp. 1163-1178
Author(s):  
Walaa H. El-Ashmawi ◽  
Diaa Salama Abd Elminaam ◽  
Ayman M. Nabil ◽  
Esraa Eldesouky

Author(s):  
Amruta More ◽  
Sheetal Vij ◽  
Debajyoti Mukhopadhyay

The research in the area of automated negotiation systems is going on in many universities. This research is mainly focused on making a practically feasible, faster and reliable E-negotiation system. The ongoing work in this area is happening in the laboratories of the universities mainly for training and research purpose. There are number of negotiation systems such as Henry, Kasbaah, Bazaar, Auction Bot, Inspire, Magnet. Our research is based on making an agent software for E-negotiation which will give faster results and also is secure and flexible. Cloud Computing provides security and flexibility to the user data. Using these features we propose an E-negotiation system, in which, all product information and agent details are stored on the cloud. This system proposes three conditions for making successful negotiation. First rule based, where agent will check user requirements with rule based data. Second case based, where an agent will see case based data to check any similar previous negotiation case is matching to the user requirement. Third bilateral negotiation model, if both rules based data and case based data are not matching with the user requirement, then agent use bilateral negotiation model for negotiation. After completing negotiation process, agents give feedback to the user about whether negotiation is successful or not. Using rule based reasoning and case based reasoning this system will improve the efficiency and success rate of the negotiation process.


Author(s):  
Pallavi Bagga ◽  
Nicola Paoletti ◽  
Bedour Alrayes ◽  
Kostas Stathis

We 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.


Author(s):  
Mandeep Mittal ◽  
Divyansh Gaba ◽  
Hemant Rana ◽  
Prabodh Ranjan Swain

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