## 4 Expert System and Decision Support System
**User Interface:** The user interface component of the expert system controls the user-system interaction and communication. It provides facilities such as menus, GUI and so on that enable a non-expert user to query the system and receive feedback. Thus, it is an agent  
Figure 4: Components or structure of an expert system. that enables the user to communicate with the expert system. The user interface function is to ease the use by developers, users and administrators of an expert system. [PERSON] and [PERSON] [120] classified expert systems into five (5) different categories: a Rule-Based Expert System, Frame-Based Expert System, Fuzzy Expert System, Neural Expert System and Neuro-Fuzzy Expert System, as described as follows. **Rule-Based Expert System:** The rule-based expert system, as the name implies, consists of a collection of rules which is an expressive way of expressing knowledge. Expert knowledge is stored specifically as a set of rules in the expert system [120]. The set of rules consists of two parts: the antecedent represented as the PART(CONDITION) and the consequent represented as the PART (action). The basic syntax for the use of rules is as follows:  
IF (antecedent)  
THEN [Consequent]  
**Frame-Based Expert System:** A frame is a structure of data that represents and models knowledge about a given entity or domain. In a frame-based expert framework, frames are used to capture and represent knowledge. Every frame has its name and an associated collection of attributes. The frame offers a natural means of expressing knowledge in a standardized and descriptive way that integrates all required information about a given object or concept. Dissimilar to the rule-based expert method where systematic searching is done for all rules, in frame-based expert system, only frame scanning is done to execute rules. **Fuzzy Expert System:** fuzzy set theory is commonly used to convey expert information that uses ambiguous and undefined terms. Fuzzy logic describes facts about entities on a sliding scale, which helps in distinguishing between class members and non-members. Fuzzy logic is also referred to as multivalued logic. Consequently, in fuzzy logic, all values of truth are represented through real numbers from 0 to 1. The probability that a given assertion is true or false is expressed by a number in the interval. The difference between the classical Boolean logic and the multi-valued fuzzy logic is depicted in Figure 5.  
**Neural Expert System:** Instead of the conventional knowledgebase, a neural expert system has a neural knowledgebase, where information is stored as neuron weights. The combination of rule extraction based on a network of neurons (neural network) provides a learning facility for the neural expert system. The neural network also enables the handling of noisy and incomplete data due to its generalization capabilities. Thus, the expert system gives room for provisional reasoning. The rule extraction unit checks the base of neural information and generates rules that are implicitly embedded in the qualified neural network. The structure of a neural expert system is shown in Figure 6.  
Figure 5: Fuzzy logic and Boolean logic. **Neuro-Fuzzy Expert System:** Fuzzy logic and neural networks are complementary natural methods in the creation of intelligent systems. Fuzzy systems cannot learn and adapt to a new environment. While neural networks can learn, they use a black box mechanism which is opaque to the end users. However, when the neural network is combined with a fuzzy group of experts, it generates more efficient technologies for designing an expert group. The integrated neuro-fuzzy system combines neural networks' parallel processing and reasoning with the human-like information representation and interpretation capabilities of fuzzy systems. Figure 7 presents the basic architecture of a neuro-fuzzy inference system.