Evaluating Degree of Dependency from Domain Knowledge Using Fuzzy Inference System

Inter-agent communication is one of the main concerns of agent oriented requirements engineering that is delineated as managing inter-dependencies and interaction among various agents performing collaborative activities. To carry out cooperative activities, tasks are disseminated and delegated to other agents with a rationale of sharing mutual expertise and potential. So an agent may be dependent on other agent for accomplishing a goal or for want of a resource to achieve that goal. This requires an agent to quantify the dependency needs termed as Degree of Dependency (DoD) to realize whether it should delegate the task to another agent, if yes, then to whom so that overall quality of Multi-Agent System (MAS) is not compromised. To evaluate DoD, this work employs domain knowledge that is an inclusive knowledge of an environment containing the business rules, credentials and reports to understand the business needs precisely. As the domain knowledge may be fuzzy and uncertain, Fuzzy Inference system is utilized to evaluate DoD from domain knowledge. This will assist the developer to address inter-agent coordination issues without squandering resources and hence in building MAS of high quality.

[1]  Manoj Kumar,et al.  Evaluation of Agent Oriented Requirements Engineering Frameworks , 2008, 2008 International Conference on Computer Science and Software Engineering.

[2]  Haruhiko Kaiya,et al.  The role of domain knowledge representation in requirements elicitation , 2007 .

[3]  Punam Bedi,et al.  Optimal Partner Selection Model for Cooperation , 2007, IICAI.

[4]  Dheeraj Kumar,et al.  Multi-Agent System Supply Chain Management in Steel Pipe Manufacturing , 2010 .

[5]  Fausto Giunchiglia,et al.  Tropos: An Agent-Oriented Software Development Methodology , 2004, Autonomous Agents and Multi-Agent Systems.

[6]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[7]  Ye-Sho Chen,et al.  Mathematical and computer modelling of the Pareto principle , 1994 .

[8]  Anuja Soni,et al.  An Integrated Approach to Prioritize Requirements using Fuzzy Decision Making , 2010 .

[9]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[10]  John Mylopoulos,et al.  Towards requirements-driven information systems engineering: the Tropos project , 2002, Inf. Syst..

[11]  Manish Kumar,et al.  A Method and Framework for Domain Knowledge Assisted Requirements Evolution (K-RE) , 2009 .

[12]  Paolo Donzelli,et al.  A goal-driven and agent-based requirements engineering framework* , 2004, Requirements Engineering.

[13]  Subramonian Sivarao,et al.  Mamdani Fuzzy Inference System Modeling to Predict Surface Roughness in Laser Machining , 2009 .