In order to enhance the efficiency in bilateral multi-issue negotiation under incomplete information, double learning algorithm that includes Q-learning algorithm and Bayesian learning algorithm is presented. The Q-le...In order to enhance the efficiency in bilateral multi-issue negotiation under incomplete information, double learning algorithm that includes Q-learning algorithm and Bayesian learning algorithm is presented. The Q-learning algorithm is used to learn the weights of issues, and the Bayesian learning algorithm is used to learn the reservation price of issues. Experiments show that the algorithm can help agents to negotiate more efficiently.展开更多
We all negotiate,formally or informally,in jobs,in day today lives and outcomes of negotiations affect those processes of life.Although negotiation is an intrinsic nature of human psyche,it is very complex phenomenon ...We all negotiate,formally or informally,in jobs,in day today lives and outcomes of negotiations affect those processes of life.Although negotiation is an intrinsic nature of human psyche,it is very complex phenomenon to implement using computing and internet for the various purposes in E Commerce.Automation of negotiation process poses unique challenges for computer scientists and researchers,so here we study how negotiation can be modeled and analyzed mathematically,what can be different techniques and strategies or set of rules/protocols to be implemented and how they can be relevantly implemented.We are in a quest to find out how this complex process,which involves human psyche can be automated using computers and modern day technologies.Now,the quest is not only automation,looking at the research in the related field in last ten years;but it is all about finding solutions to make e-negotiation more efficient and more accurate,as well as useful in any kind of electronic trading situations.Here is an attempt to consolidate our work of last few years on automation of negotiation process;we call it as negotiation protocol on research,study as well as implementation level of negotiation automation.Overall,we are trying to give few solutions to make the automation more efficient.展开更多
Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated ap- proaches have proven particularly promising for complex ne- gotiations and previous research indicates evolutio...Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated ap- proaches have proven particularly promising for complex ne- gotiations and previous research indicates evolutionary com- putation could be useful for such complex systems. To im- prove the efficiency of realistic multi-lateral multi-issue ne- gotiations and avoid the requirement of complete informa- tion about negotiators, a novel negotiation model based on art improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotia- tion efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution （DE）, in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive pa- rameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multi- agent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.展开更多
基金by the Ministerial Level Advanced Foundation(41325081)
文摘In order to enhance the efficiency in bilateral multi-issue negotiation under incomplete information, double learning algorithm that includes Q-learning algorithm and Bayesian learning algorithm is presented. The Q-learning algorithm is used to learn the weights of issues, and the Bayesian learning algorithm is used to learn the reservation price of issues. Experiments show that the algorithm can help agents to negotiate more efficiently.
文摘We all negotiate,formally or informally,in jobs,in day today lives and outcomes of negotiations affect those processes of life.Although negotiation is an intrinsic nature of human psyche,it is very complex phenomenon to implement using computing and internet for the various purposes in E Commerce.Automation of negotiation process poses unique challenges for computer scientists and researchers,so here we study how negotiation can be modeled and analyzed mathematically,what can be different techniques and strategies or set of rules/protocols to be implemented and how they can be relevantly implemented.We are in a quest to find out how this complex process,which involves human psyche can be automated using computers and modern day technologies.Now,the quest is not only automation,looking at the research in the related field in last ten years;but it is all about finding solutions to make e-negotiation more efficient and more accurate,as well as useful in any kind of electronic trading situations.Here is an attempt to consolidate our work of last few years on automation of negotiation process;we call it as negotiation protocol on research,study as well as implementation level of negotiation automation.Overall,we are trying to give few solutions to make the automation more efficient.
文摘Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated ap- proaches have proven particularly promising for complex ne- gotiations and previous research indicates evolutionary com- putation could be useful for such complex systems. To im- prove the efficiency of realistic multi-lateral multi-issue ne- gotiations and avoid the requirement of complete informa- tion about negotiators, a novel negotiation model based on art improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotia- tion efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution （DE）, in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive pa- rameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multi- agent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.
