doi: 10.7763/IJET.2011.V3.205
Implementing a Decision Support Module in Distributed Multi-Agent System for Task Allocation Using Granular Rough Model
Sally M. El-Ghamrawy
,
Ali I. El-Desouky
,
- the Computers and Systems Department, Faculty of Engineering, Mansoura University, Egypt
Abstract
A Multi-Agent System (MAS) is a branch of distributed artificial intelligence, composed of a number of distributed and autonomous agents. In MAS, an effective coordination is essential for autonomous agents to reach their goals. Any decision based on a foundation of knowledge and reasoning can lead agents into successful cooperation, so to achieve the necessary degree of flexibility in coordination, an agent requires making decisions about when to coordinate and which coordination mechanism to use. The performance of any MAS depends directly with the right decisions that the agents made. Therefore the agents must have the ability of making right decisions. In this paper, we propose a decision support module in a distributed multi-agent system, which enables any agent to make decisions needed for Task allocation problem; we propose an algorithm for Task Allocation Decision Maker (TADM) based on Granular Rough Model (GRM). Furthermore, a number of experiments were performed to validate the effectiveness of the proposed algorithm (TADM)); we compare the efficiency of our algorithms with recent frameworks. The preliminary results demonstrate the efficiency of our algorithms
Keywords
- Decision Making
- Task allocation
- Coordination Mechanism
- Multi-Agent System (MAS)
- Rough Set
- Granular Computing
How to Cite
Sally M. El-Ghamrawy, Ali I. El-Desouky, and Mostafa Saleh, "Implementing a Decision Support Module in Distributed Multi-Agent System for Task Allocation Using Granular Rough Model," International Journal of Engineering and Technology, vol. 3, no. 1, pp. 85-95, 2011. https://doi.org/ 10.7763/IJET.2011.V3.205
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).