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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 106-113 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.492

The Comparison between Forward and Backward Chaining

Ajlan Al-Ajlan
Abstract—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.

Ajlan Al-Ajlan is with the Department of Information System Management, Qassim University, Saudi Arabia (e-mail: aajlan@qu.edu.com).

[PDF]

Cite: Ajlan Al-Ajlan, "The Comparison between Forward and Backward Chaining," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 106-113, 2015.

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