# A Rule-Based System for Human Performance Evaluation: A Case Study
## 1 Introduction  
According to [1], organizations put significant effort into improving the quality of their products and services. Efficient and objective performance evaluation could lead to better and higher quality processes [2]. The performance of organizations is significantly affected by employee performance. Individual performance is a key indicator of the company and contributes to the company's productivity and competitiveness [3]. Human performance evaluation is an extremely complex problem within most organizations since human judgment has subjective characteristics. Therefore, in order to provide valid decision-making, it is of utmost importance to provide an efficient, transparent, and objective performance evaluation system. The best way to achieve these objectives is to propose the implementation of modern technology and reasoning into the process itself. Modern technology is driven by exact expectations, and it entirely alters the way processes usually flow. This leads to the importance of making real-time decisions. In the past, persons having experience within a certain area were the ones that made the decisions. That ability made them professionals in specific areas of work, i.e., experts. However, the tendency was to inject such capacities of thinking and reasoning into a computer to enable decision-making to less qualified persons. Those were the reasons why back-up systems came into existence. These systems are called decision-making support systems (DMSS). They were a basis for the development of expert systems (ES). The ES was originally introduced as a branch of applied artificial intelligence by [PERSON] in the 1970s [4]. They are defined as software apps that are capable of solving specific problems in a way human experts do. Thus, their ability is to perform at a level close to the human expert performance [5]. They are, therefore, highly useful information systems for making decisions in different areas, including in the field of mechanical engineering, science, healthcare, and others. ES are mostly based on rules that represent knowledge from a particular problem area, and it is entered by the user in the knowledge base. In this manner, the user is provided with significant assistance in implementing the expert system within the process since the only task when applying such a system is to add knowledge in the form of rules [6]. The most popular types of expert systems currently applied include Visual Basic, JESS, CLIPS, MATLAB, PyKE, and VisiRule. VisiRule represents the expert system shell [7]. It is a part of the software that contains the user interface, the format for displaying the knowledge base within the specific domain, as well as the inference engine. The inference engine, as the most important ES component, makes a decision when a certain rule needs to be fired. Its purpose is to compare each rule stored in the knowledge base (rule base) with the facts stored in the database. There are two methods of executing a rule-forward chaining and backward chaining, which fire the rules in different manners [6]. Forward chaining is based on data, and it applies rules to make conclusions or new facts by using a top-down approach. Backward chaining is based on objectives to determine which data should be highlighted, using a bottom-up approach [7]. This paper proposes the application of a rule-based ES within a process of making decisions regarding the performance of periodical technical inspection (PTI) inspectors in Croatia. The study is a continuation of the preliminary research conducted in [8], which concluded that by designing rule-based ES, it is possible to achieve the standardization of the process and reduce or even eliminate all types of lean wastes within the process. Moreover, it was found that VisiRule ES shell application within the decision-making process can ease the implementation process and achieve greater efficiency.