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Grid technology allows resource sharing among several entities, but selecting the best resource to run a specific job remains one of its main problems. The challenges for the best resource selection involve analysis of several factors such as prediction time to run a job, access restriction to resources, and cost to use resources. This paper presents a resource selection model using decision theory for combining these influential factors in the resource selection process. The model is based on a multi-attribute utility function that maximizes the user preference. This model forms part of a multi-agent system deploying distributed and parallel processing for job execution prediction, access policy verification, and resource selection models. All models of this paper were verified through experiments using real machines. They have presented appropriate functional behaviors and positive performance results.