We propose a system and method for a robot assistant for teaching multi-attribute decision making (MCDM). Through questions and answers in natural language, the robot assistant learns the user’s preferences on multiple criteria involving a selection decision and makes recommendations using data on each criterion and the learned user preferences. It will include a use-case demonstration where NAO the robot will assist a human in forming a simple portfolio of mutual funds. Presenters will illustrate the architecture of the robot assisted MCDM and describe a method that is extensively used to structure complex decision problems and has been applied to a variety of problems in a diverse set of disciplines, such as selecting a project, selecting a life insurance contract, selecting public relations firms, deciding on library acquisitions, hostage negotiations, selecting sites for wildlife management, and selecting a nonprofit for donation
