Requirements elicitation is defined as a process of collecting the requirements of a system from users, customers and other stakeholders and also referred as requirement gathering. Requirements elicitation is non-trivial because there is no well-defined technique or process to get all requirements from the user and customer by just asking them.Different AI techniques are used to optimize the requirement elicitation process. Process optimization is the discipline of adjusting a process so as tooptimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost, maximizing throughput, and/or efficiency. Many software projects are failed to ambiguous or ill-defined requirements. Optimization is a way of achieving optimum of anything. In this paper a literature review is presented on different optimization techniques and a hybrid approach is introduce for optimizing the requirements elicitation process. Bayesian network is an effective uncertain knowledge representation and reasoning method. Fuzzy sets can be used for expressing fuzzy events or fuzzy objectives in some special region. Combining these two theories a hybrid inference system with fuzzy sets and Bayesian networks which are called "Fuzzy Bayesian Networks (FBNs)” is presented.
