multiple objective programming (mop) problems have become famous among many researchers due to more practical and realistic implementations. there have been a lot of methods proposed especially during the past four decades. in this paper, we develop a new algorithm based on a new approach to solve mop problems by starting from a utopian point (which is usually infeasible) and moving towards the...

  In this paper imprecise target models has been proposed to investigate the relation between imprecise data envelopment analysis (IDEA) and mini-max reference point formulations. Through these models, the decision makers' preferences are involved in interactive trade-off analysis procedures in multiple objective linear programming with imprecise data. In addition, the gradient projection type...

Problem-solving procedures have been typically aimed at achieving well-defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with guided by sets of controls that define different dimensions quality. We explore methods enable people to express preferences about the operation classification models in supervised multic...

Despite its ubiquity in human learning, very little work has been done in artificiaJ intelligence on agents that learn from interactive natural language instructions. In this paper, we examine the problem of learning procedures from interactive, situated instruction, in which the student is attempting to perform tasks within the instructional domain, and asks for instruction when it is needed. ...