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KEMNAD: A KNOWLEDGE ENGINEERING METHODOLOGY FOR NEGOTIATING AGENT DEVELOPMENT
Author(s) -
Luo Xudong,
Miao Chunyan,
Jennings Nicholas R.,
He Minghua,
Shen Zhiqi,
Zhang Minjie
Publication year - 2012
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00409.x
Subject(s) - computer science , negotiation , task (project management) , modular design , domain (mathematical analysis) , variety (cybernetics) , domain knowledge , quality (philosophy) , software engineering , human–computer interaction , knowledge management , artificial intelligence , systems engineering , engineering , programming language , mathematical analysis , philosophy , mathematics , epistemology , political science , law
Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardized components rather than reinventing the wheel each time. Moreover, because these patterns are identified from a wide variety of existing negotiating agents (especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system.