Reliability analysis of mobile agent-based systems
Proceedings of the 2005 ACM symposium on Applied computing
The Hopfield-Tank neural network applied to the mobile agent planning problem
Applied Intelligence
Monte Carlo simulation-based algorithms for estimating the reliability of mobile agent-based systems
Journal of Network and Computer Applications
Information Sciences: an International Journal
Statistical behaviors of mobile agents in network routing
The Journal of Supercomputing
Efficient dynamic itinerary and memory allocation for mobile agents
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Recently, the distributed agent concept has become a new computing paradigm in the Internet distributed computing, including the mobile computing. Mobile agent planning is one of the most important techniques for completing a given task efficiently. The static planning technique may not be the best approach in real network environments. This is mainly due to the fluctuation of network traffic, that is, connection failures or heavy traffic on the network. For better performance, it is necessary that mobile agents be more sensitive to the network conditions.In this paper, we propose a dynamic planning algorithm, named n-ary agent chaining, which is based on static mobile agent planning. Mobile agents can change their itinerary dynamically according to current network status using the proposed algorithm. The proposed algorithm also takes into account the locality of target nodes on the network. Thus, with a properly chosen locality factor, it can adapt to realistic network situations. Using an agent reproduction technique, the nodes, not processed by the original agent, obtain a second chance to be visited. Agents reproduced from the original one, named cloned agents, processthe unprocessed nodes in the proposed algorithm. Since the turn-around time can be calculated mathematically with known network statistics before launching the agents, the proposed algorithm is suitable for agent problem domains with deadline constraints.