An agent architecture for managing data resources in a grid environment

The agent paradigm has been successfully used in a large number of research areas. MAPFS, a parallel file system, constitutes one successful application of agents to the I/O field, providing a multiagent I/O architecture. The use of a multiagent system implies coordination and cooperation among its agents. MAPFS is oriented to clusters of workstations, where agents are applied in order to provide features such as caching or prefetching. The adaptation of MAPFS to a grid environment is named MAPFS-Grid. Agents can help to increase the performance of data-intensive applications running on top of the grid.

This paper describes the conceptual agent framework and the communication model used in MAPFS-Grid, which provides the management of data resources in a grid environment. The evaluation of our proposal shows the advantages of using agents in a data grid.

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).