The Comparison between Forward and Backward Chaining (original) (raw)

2015, International Journal of Machine Learning and Computing

Nowadays, more and more students all over the world need expert systems, especially in academic sectors. They need advice in order to be successful in their studies, and this advice must be provided by the academic management. There are two reasoning strategies in expert system, which have become the major practical application of artificial intelligence research: forward chaining and backward chaining. The first one starts from the available facts and attempts to draw conclusions about the goal. The second strategy starts from expectations of what the goal is, and then attempts to find evidence to support these hypotheses. The aim of this paper is to make a comparative study to identify which reasoning strategy system (forward chaining or backward chaining) is more applicable when making evaluations in expert management, especially in the academic field. Index Terms-Artificial intelligence, expert system, forward and backward chaining, state space. I. INTRODUCTION In the academic field, some students need the best advice in order to improve their situation, and this advice must be provided by the academic management. The academic management should consider many important factors when providing such advise and some of these factors are the mode of study (part-time or full-time), is the student working or not, the number of courses taken last semester, the CGPA of last semester etc. In addition, as modern society has become more specialized, the need to have "expert systems" is increasing rapidly. These needs are visible in all areas of life [1]. These days, humans provide most "expert advice". They have initiative and intelligence to adapt to a new model automatically and made decisions based on new data rapidly. However, humans also take a long time to learn things and can only perform one complete task at a time. For some of the reasons mentioned above, many companies have attempted to amass and collate the knowledge of experts about a special problem area. Some other reasons are as follows: (1) the knowledge of people who have particular expertise can be stored before they retire, so the company does not lose their knowledge and (2) the implementation of expert systems lightens the load on specialists. Such systems can be created to solve routine problems more easily and quickly [2]-[4]. The aim of this paper is to make a comparative study between forward-chaining and backward-chaining to select which of them is more applicable to the management expert Manuscript