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Finding the right candidate for a job has always been a hard task that Human Resources (HR) managers of a company face regularly. In this paper, the authors propose that the field of multi-agents can play a significant role in a) elaborating the job description b) getting an applicant to submit competencies relevant to the job c) shortlisting applicants and d) identifying the right hire. They propose the model of (HR)^2, an automated agent for Helping HR with Recruitment that could perform the following key steps: (a) Generate Specific Position Contract (SPC) from a Master Position Contract (MPC) using Infer1 procedure (b) Use the SPC to provide a graded and iterative feedback to applicant using Infer2 procedure. They situate (HR)^2 in the context of LinkedIn. To enable better inference, they propose to modify the information being collected by LinkedIn, using the ontology provided by the free online database O*NET. The (HR)^2 agent will be able to help the employer rank order the SPCs and identify areas for assessment, potentially easing the interview process and leading to high quality hires.

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