Mobile agents and the future of the internet
ACM SIGOPS Operating Systems Review
IEEE Spectrum
An agent-based approach for building complex software systems
Communications of the ACM
Timed mobile agent planning for distributed information retrieval
Proceedings of the fifth international conference on Autonomous agents
Addressing the complexity of patient monitoring with multiagents and modular logic
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Cost Effective Mobile Agent Planning for Distributed Information Retrieval
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
A mobile robot for corridor navigation: a multi-agent approach
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Architecture for Automated Annotation and Ontology Based Querying of Semantic Web Resources
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
A fuzzy logic approach to forecast energy consumption change in a manufacturing system
Expert Systems with Applications: An International Journal
Proceedings of the 2007 Summer Computer Simulation Conference
Self and non-self discrimination agents
Proceedings of the 2008 ACM symposium on Applied computing
Agent and ontology based information gathering on restricted web domains with AGATHE
Proceedings of the 2008 ACM symposium on Applied computing
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
Self-managing agents for dynamic scheduling in manufacturing
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A survey of recent advances in fuzzy logic in telecommunications networks and new challenges
IEEE Transactions on Fuzzy Systems
Hi-index | 12.05 |
This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one-output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller's output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network.