There are several research areas of AI, and expert systems are one of the prominent areas among them. This is the most successful demonstration of AI capabilities that represents truly commercial application of the work done in the field of AI. Like other AI programs, expert systems are computer programs that simulate the human expert thought process to solve complex decision problems in a specific domain. 

In other words, an expert system is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise. The power of expert systems comes primarily from the expert system’s knowledge base that stores specific knowledge about a narrow domain. Unlike other streams of AI, expert systems do not have human capabilities. It is the knowledge base that works as the center of a particular domain. Also, the knowledge base of an expert system also contains heuristic knowledge – rules of thumb used by human experts who work in the domain.

This interesting AI system began as a special branch of AI in the late 1960s to early 1970s. However, it has grown dramatically in the past few years. Knowledge-based expert systems typically deal with diagnostic/prescriptive types of problems. Here are some of the examples – 

MYCIN – This backward chaining-based expert system can identify various bacteria that can cause severe infections and recommend drugs based on the person’s weight.

DENDRAL –This expert system is used for chemical analysis. It used a substance’s spectrographic data to predict its molecular structure.

PXDES – It could easily determine the type and the degree of lung cancer in a patient based on the data.

R1/XCON – It could select specific software to generate a computer system wished by the user.

CaDet – It is a clinical support system that could identify cancer in its early stages in patients.

DXplain – It was also a clinical support system that could suggest a variety of diseases based on the doctor’s findings.

Expert systems programming differ from traditional computer programming in several ways.

Related post – Natural language processing and its impact on AI

Components of an Expert System : 

Knowledge Base

The knowledge base of an expert system contains both factual and heuristic or rules knowledge. It consists of knowledge in a particular domain as well as rules to solve a problem, procedures, and intrinsic data relevant to the domain. For knowledge base, knowledge representation can be drawn upon in several ways. Two of these methods include:

1. Frame-based systems

It helps to build very powerful expert systems. A frame specifies the attributes of a complex object, and frames for various object types have specified relationships.

2. Production rules

These are the most common knowledge representation method. Rule-based expert systems are expert systems in which production rules represent the knowledge.

Inference Engine

The inference engine fetches the relevant knowledge from the knowledge base, interprets it, and finds a solution relevant to the user’s problem. The inference engine acquires the rules from its knowledge base and applies them to the known facts to infer new facts. Inference engines can also include explanation and debugging abilities. In a rule-based expert system, the inference engine controls the order in which production rules are applied. Besides, the inference engine directs the user interface to query the user for any information it needs for further inferencing.

In this case, the working memory works as a blackboard that accumulates knowledge about the case at hand. The inference engine repeatedly adds new information to it until a goal state is reached.

Knowledge Acquisition and Learning Module

 This component allows the expert system to acquire more and more knowledge from various sources and store it in the knowledge base.

User Interface

Through this module, a non-expert user can interact with the expert system and find a solution to the problem.

Explanation Module

This module helps the expert system to give the user an explanation about how the expert system reached a particular conclusion.

How is knowledge acquired from the knowledge base?

The Inference Engine acquires knowledge from the Knowledge Base using two strategies, namely – 

• Forward Chaining
• Backward Chaining

Forward Chaining – 

This is a strategic process in which the Expert System answeres the questions – What will happen next. This strategy mostly manages tasks like creating a conclusion, result, or effect: prediction or share market movement status.

Backward Chaining – 

This is basically storage used by the Expert System to answer the questions – Why this has happened. This strategy is mostly used to find out the root cause or reason behind it, considering what has already happened – diagnosis of stomach pain, blood cancer or dengue, etc. 

 Characteristics of an Expert System : 

• Human experts are not permanent, but an expert system is permanent.
• One expert system can be more efficient as it may contain more than one human expert knowledge.
• It helps to distribute the expertise of a human.
• Widely used in medical diagnosis, the expert system decreases the cost of consulting of an expert.
• It is based on the knowledge base and inference engine.
• By deducing new facts through existing facts of knowledge, expert systems can solve complex problems. It uses if-then rules rather than conventional procedural code.
• Expert systems are among the first truly successful forms of artificial intelligence (AI) software.

Limitations : 

• Do not have human-like decision-making power.
• Cannot produce correct results from less amount of knowledge.
• Cannot possess human capabilities.
• Requires excessive training.

Advantages : 

• Low accessibility cost.
• Not affected by emotions, unlike humans.
• Fast response.
• Low error rate.
• Capable of explaining how they reached a solution.

Disadvantages : 

• The expert system has no emotions.
• It is developed for a specific domain.
• Common sense is the main issue of the expert system.
• Not capable of explaining the logic behind the decision.
• It needs to be updated manually. It does not learn itself.

Applications :

 The application of an expert system can be found in almost all areas of business or government. They include areas such as –

• Different types of medical diagnoses like internal medicine, blood diseases, and show on.
• Diagnosis of the complex electronic and electromechanical system.
• Diagnosis of a software development project.
• Planning experiments in biology, chemistry, and molecular genetics.
• Forecasting crop damage.
• Diagnosis of the diesel-electric locomotive system.
• Identification of chemical compound structure.
• Scheduling of customer orders, computer resources, and various manufacturing tasks.
• Assessment of geologic structure from dip meter logs.
• Assessment of space structure through satellite and robot.
• The design of the VLSI system.
• Teaching students specialize tasks.
• Assessment of log, including civil case evaluation, product liability, etc.

Final verdict

Like other myths of AI, expert systems have also created several debates about humanity’s future and its improvement. This is related to its intelligence. However, it is time to wait and watch to see what expert systems can perform in the future for humanity.