Expert systems are a branch of artificial intelligence that aim to provide computerized decision-making capabilities similar to those of a human expert in a specific domain. They are designed to solve complex problems using a set of rules or algorithms that can mimic human reasoning processes.
In this tutorial, we’ll overview the expert systems, their components, working mechanisms, types, the process of building an expert system, their applications, and their advantages and limitations.
An expert system is an artificial intelligence system that performs a specific task with expert-like efficiency by applying predefined rules to analyze information and generate conclusions. The expert system can provide specialist advice or decision-making automation, assist in problem-solving, and help identify errors or risks.
They incorporate rules and algorithms that allow them to perform tasks and make decisions based on a knowledge base filled with expert knowledge, heuristics, and reasoning patterns. Expert systems are widely used in various industries, including healthcare, finance, engineering, and many others.
3. Components of Expert Systems
There are four main components of expert systems.
3.1. Knowledge Base
The knowledge base is the core of an expert system. It contains a set of rules, facts, and heuristics that represent the knowledge of a human expert in a specific domain. The domain expert with the required expertise and experience creates the knowledge base. It is encoded in a formal language that the expert system can understand.
3.2. Inference Engine
The inference engine is responsible for reasoning and decision-making. It applies the rules and procedures defined in the knowledge base to the input data and generates the required output. The inference engine uses various reasoning techniques, including backward and forward chaining, to deduce conclusions from the knowledge base.
3.3. User Interface
The user interface is the communication channel between the user and the expert system. It presents the questions, options, and recommendations generated by the inference engine in a user-friendly way. The user interface can be text-based or graphical, depending on the application.
3.4. Explanation Module
The explanation module is used to explain the reasoning process of the expert system. The explanation module explains how the expert system arrived at a conclusion or recommendation.
4. The Working Mechanism of Expert Systems
Expert systems work by combining the knowledge base, the inference engine, the user interface, and the explanation facility. The reasoning process begins with the user providing input data to the expert system through the user interface.
The inference engine then uses the rules and heuristics stored in the knowledge base to generate conclusions and recommendations based on the input data. The explanation facility provides the user with a detailed explanation of the reasoning process and how the expert system arrived at its conclusion:
Expert systems gather and place information and facts in a knowledge base and integrate them with an inference engine. The inference engine uses two methods to acquire information from the knowledge base.
In forward chaining, the expert system reads and processes a set of facts to logically predict what will happen next. An example of this would be predicting stock market movements.
In backward chaining, the system reads and evaluates a set of information backward to reach a logical conclusion about a goal. For example, prediction in a medical diagnosis system.
5. Types of Expert Systems
There are various types of expert systems with their unique strengths and weaknesses, and they are used in various applications in multiple industries.
5.1. Rule-based Expert Systems
Rule-based expert systems are the most common type of expert system. They use a set of rules to reason about a problem and provide solutions or recommendations. These rules are created by human experts and are organized in a knowledge base.
5.2. Fuzzy Logic Expert Systems
Fuzzy logic expert systems use fuzzy logic to handle uncertainty and imprecision in data. Fuzzy logic is a mathematical framework that allows for degrees of truth instead of the traditional binary (true or false) approach. Fuzzy expert systems are used in product recommendation systems and image recognition applications.
5.3. Knowledge-based Expert Systems
Knowledge-based expert systems use a knowledge base that contains facts and rules about a specific domain. These systems are designed to mimic the problem-solving capabilities of human experts. They use a knowledge inference engine to explain the problem and provide solutions.
5.4. Neural Networks Expert Systems
Neural network expert systems are designed to learn from data by adjusting the weights of their connections between neurons. They are used in speech recognition, image classification, and natural language processing applications.
6. Development of Expert Systems
Knowledge engineering refers to building and maintaining an expert system. The diagram below represents various steps in the development of an expert system:
First, we acquire knowledge by gathering information from human experts. This process involves various techniques, including interviews, questionnaires, and observation. In the second step, we’ll transform acquired knowledge into a format the system can understand and use. This format may take various forms, such as rules, frames, semantic networks, or ontologies, depending on the nature of the knowledge and the system’s requirements.
In the third step, we’ll ensure that the knowledge in the expert system is accurate and complete. This can involve testing the expert system against known data or comparing its recommendations to those of human experts. Once validated, we’ll implement the system using an inference engine.
Finally, we maintain our expert system’s knowledge base by regularly updating it with new information. This process includes adding new rules, modifying existing ones, or removing outdated information. Overall, the knowledge engineering process is iterative, and each step may need to be revisited and refined as the development of the expert system progresses.
7. Application of Expert Systems
Expert systems have numerous applications in various industries, including healthcare, finance, engineering, and many others:
- Medical Diagnosis: The expert system can help medical professionals diagnose diseases, analyze symptoms, and provide treatment options based on medical history, symptoms, and test results. For example, MYCIN is an expert system for diagnosing bacterial infections
- Financial Planning: The expert system can assist in financial planning, such as investment strategy, retirement planning, and tax planning, based on a user’s financial situation and goals. For example, LenddoEFL is a digital financial inclusion platform that uses machine learning to evaluate the credit risk of individuals and small businesses
- Quality Control: The expert system can help identify defects or quality concerns by analyzing production data or testing results. An example is the DENDRAL expert system for chemical structure determination
- Customer Support: The expert system can help customers troubleshoot technical issues or provide product-related advice based on a user’s queries or inputs. An example expert system is XCON, used for configuring computer systems
8. Advantages and Limitations of Expert Systems
Let’s look at the advantages and limitations of expert systems:
In this tutorial, we learn about expert systems, their types, working mechanism, applications, advantages, and limitations.
Expert systems have become an integral part of many industries because they provide quick, accurate, and consistent decision-making automation. In addition, their ease of use and flexibility make them accessible to non-experts in the domain, reducing the dependence on human experts.