KEMNAD:a knowledge engineering methodology for negotiating agent development

Article, Unknown English OPEN
Luo, Xudong ; Miao, Chunyan ; Jennings, Nick ; He, Minghua ; Shen, Zhiqi ; Zhang, Minjie (2012)
  • Publisher: Wiley
  • Related identifiers: doi: 10.1111/j.1467-8640.2012.00409.x
  • Subject: knowledge engineering | ORIENTED SOFTWARE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREFERENCES | Artificial Intelligence & Image Processing | ISSUE TRADE-OFFS | Technology | AUTOMATED NEGOTIATIONS | e-business | agents | INTELLIGENT AGENTS | automated negotiation | Science & Technology | Computer Science | 0801 Artificial Intelligence And Image Processing | 1702 Cognitive Science | DECISION-MAKING | GAIA METHODOLOGY | 0802 Computation Theory And Mathematics | software engineering | BARGAINING MODEL | ELECTRONIC COMMERCE | MULTIAGENT SYSTEMS

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 standardised components rather than reinventing the wheel each time. Moreover, since 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.
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