Title
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Abstract
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. © 2012 Wiley Periodicals, Inc.
Year
DOI
Venue
2012
10.1111/j.1467-8640.2012.00409.x
Computational Intelligence
Keywords
Field
DocType
knowledge engineering methodology,automated negotiation,various domain,new methodology,human user,complex knowledge,high impact,final system,relevant domain knowledge,architectural pattern,domain knowledge,negotiating agent development,negotiating,software engineering,standardisation,methodology,design pattern,engineering,knowledge engineering,agent,agents,knowledge,e business
Reinventing the wheel,Intelligent agent,Domain knowledge,Computer science,Multi-agent system,Knowledge engineering,Artificial intelligence,Modular design,Architectural pattern,Machine learning,Design pattern
Journal
Volume
Issue
ISSN
28
1
0824-7935
Citations 
PageRank 
References 
33
1.00
58
Authors
6
Name
Order
Citations
PageRank
Xudong Luo177364.70
Chunyan Miao22307195.72
Nicholas R. Jennings3193481564.35
Minghua He457738.37
Zhiqi Shen5114882.57
Minjie Zhang625530.01