Expert System in Artificial Intelligence: Key Concepts and Applications

Expert systems in artificial intelligence are computer programs designed to mimic human expertise in a specific domain. They combine a knowledge base with reasoning rules to provide intelligent decision-making and problem-solving. Unlike general AI systems, expert systems focus on narrow fields, delivering consistent and explainable results that make them highly valuable across industries. 

With key characteristics of expert systems in Artificial Intelligence, such as reliability, efficiency, and adaptability, these systems ensure accurate decision-making across various domains.

In this guide, you’ll read more about what an expert system in AI is, its key components, and defining characteristics. We’ll also explore the types of expert systems, their architecture, and how they work. You’ll discover real-world applications, advantages, and limitations, along with a comparison to other AI systems. Finally, we’ll look at the future of expert systems and answer common FAQs to give you a complete understanding. 

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What is an Expert System in Artificial Intelligence?

An expert system in artificial intelligence is a computer program designed to imitate the decision-making ability of a human expert. Instead of relying only on raw data, it uses stored knowledge and logical reasoning rules to solve problems within a specific domain. For example, in healthcare, an expert system can suggest possible diagnoses by applying medical knowledge, just like a doctor would. 

The concept of expert systems began in the 1960s and gained momentum in the 1980s. Early examples like MYCIN (for medical diagnosis) and DENDRAL (for chemical analysis) showed that computers could capture expert reasoning in a structured way. These systems highlighted that knowledge-driven approaches could be just as valuable as data-driven methods. 

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Characteristics of Expert Systems

An expert system possesses features like a knowledge base, inference engine, and learning capability, which together provide advice based on pre-defined rules and facts.

Here are the characteristics of an expert system in artificial intelligence.

• Knowledge base: The system contains facts and rules about a particular domain.
• Inference engine: The system applies logical rules to the knowledge base to derive conclusions.
• User interface: Enables the system to interact with humans.
• Explanation facility: Gives an explanation for its actions.
• Learning Capability: The system can improve performance over time, depending on the system design.

Capabilities of Expert Systems in Artificial Intelligence

The main function of expert systems is to replace human expertise in solving complex problems. In addition, it can perform functions like error detection and conflict resolution.

Here’s how you can solve problems by the application of an expert system.

• Advising: Expert systems can give advice based on expert knowledge.
• Instructing and assisting humans in decision-making: Expert systems can help users navigate complex decisions.
• Demonstrating: The system can give step-by-step solutions or actions.
• Deriving a solution: The system can analyze available data to suggest viable solutions.
• Diagnosing: It identifies issues or problems by analyzing symptoms.
• Explaining: It explains why a certain decision was made or why an option is recommended.
• Interpreting input: It can understand and process user queries or data inputs.
• Predicting results: It can predict potential outcomes based on existing knowledge.
• Justifying the conclusion: The system can provide reasons for the selected decision.
• Suggesting alternative options: It provides multiple choices for solving a problem.

Types of Expert Systems in Artificial Intelligence

Expert systems in AI can be categorized into different types based on their structure and functionality. Each type is designed to solve specific kinds of problems, such as pattern recognition.

You can divide an expert system in artificial intelligence into the following types.

• Rule-based expert systems: The system makes use of "if-then" rules to make decisions based on the input provided.
• Frame-based expert systems: The system organizes knowledge in structures called frames to show objects and their relationships.
• Fuzzy logic systems: Allows the system to reason with imprecise or uncertain information using degrees of truth rather than just true/false.
• Neural network-based expert systems: The system uses neural networks for pattern recognition and decision-making.
• Neuro-fuzzy expert systems: Combine fuzzy logic with neural networks to tackle uncertainty and learn from data effectively.          

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After a brief overview, let’s look at the structure of an expert system in artificial intelligence.           

Components of Expert Systems in Artificial Intelligence

You can break down the expert system in artificial intelligence into the following components.

Knowledge Base

The knowledge base is the core component of an expert system. It is made up of the facts, rules, heuristics, and procedures that the system uses to make decisions or solve problems. As the system encounters new situations, the knowledge base can be continuously updated with new information.

The knowledge base is usually represented in the following structure.

• Rules: "If-Then" statements are used to define relationships between facts and conclusions.
• Frames: Data structures that refer to the objects and their attributes.
• Schemas: Represent a category or class of objects or concepts.

Interface Engine

The inference engine is the processing unit that analyzes and applies the knowledge stored in the knowledge base. It uses logical reasoning to make decisions based on the given input. 

The inference engine operates in two modes:

• Forward Chaining: Begins from known facts and applies inference rules to deduce new facts or conclusions.
• Backward Chaining: Begins with a goal or hypothesis and works backward through the rules to find the facts that support it.

User Interface

The user interface enables the user to interact with the expert system. It allows users to input data, query the system, and receive recommendations. The interface ensures the system is user-friendly and accessible.

The user interface component provides functions such as:

• Input forms for collecting user data.
• Display screens for presenting results or suggestions.
• Help functions guide the user in using the system.

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Now that you have an idea about the structure, let’s look at how an expert system in artificial intelligence works.

How Expert System in Artificial Intelligence Work?

An expert system in Artificial Intelligence uses a combination of structured knowledge and logical reasoning to generate solutions based on user input. The process can be mainly divided into two stages: Knowledge gathering and decision-making.

Here’s the process of knowledge gathering and encoding.

• Knowledge acquisition

In this step, the system gathers knowledge from human experts, databases, scientific literature, and other resources. The collected knowledge is encoded in the form of rules, frames, facts, or ontologies, depending on the type of expert system.

• Knowledge encoding

After collection, the knowledge is structured in a format that the system can understand and process. This is done by converting the acquired information into rules (Ex: "If condition X is true, then Y action should be taken") or frames ( entities and their properties).

• Updating knowledge base

The knowledge base is continuously updated as new information becomes available. This allows the expert system to adapt to new scenarios and challenges.

Here’s how inference engines operate in decision-making.

• Applying rules

Based on the user's input and the facts in the knowledge base, the inference engine applies relevant rules to arrive at conclusions.

• Forward chaining

By starting with known facts, the inference engine uses rules to deduce new information until a solution is obtained.

• Backward chaining

By starting with a goal or conclusion, the inference engine works backward through the rules to find the facts that support the conclusion.

Here’s a real-world example of an expert system in artificial intelligence.

• MYCIN is one of the earliest and most famous expert systems, developed in the 1970s for medical diagnosis.
• The main objective of MYCIN is to diagnose bacterial infections and recommend antibiotics.
• The knowledge base of MYCIN contains rules about diseases, symptoms, and appropriate treatments.
• The system works by asking the doctor a series of questions to determine the infection type and severity.

You can represent the workings of an expert system in Artificial Intelligence through a flow chart.

• User input: The user provides information to the system about the problem or situation.
• Knowledge base: The system refers to the relevant facts, rules, and data from the knowledge base.
• Inference engine: Based on the given input, the inference engine applies relevant rules to make deductions or generate conclusions.
• Output: The system provides a solution, recommendation, or diagnosis.
• Explanation: The system may also explain how it arrived at the conclusion.
• User interaction: The user can provide additional input, or the system may ask for more information to refine the decision.

Also Read: 5 Significant Benefits of Artificial Intelligence

After understanding the workings of an expert system in artificial intelligence, let’s check the reasoning strategies used by the system.

What are the Reasoning Strategies used by the Inference Engine?

The inference engine is one of the core components of the expert system. The main objective of the inference engine is to process the knowledge stored in the knowledge base and generate conclusions or solutions. 

Here are the two commonly used reasoning strategies.

Forward Chaining

This data-driven reasoning strategy starts with the available facts and applies inference rules to arrive at new facts or conclusions. The system works from known data (facts) towards a conclusion.
Here’s how forward chaining works.

• Begin with facts

The system starts with the known facts, which are gathered from the user input or predefined conditions in the knowledge base.

• Apply rules

The inference engine applies the relevant rules from the knowledge base. Each rule checks whether its conditions (the "If" part) are satisfied by the available facts.

• Generate new facts

When a rule's conditions are satisfied, the "Then" part of the rule is triggered, which generates new facts or conclusions.

• Repeat

The newly generated facts are used in subsequent rule evaluations. This process continues until the goal is achieved or no more rules can be applied.

Here’s a real-world example of medical diagnosis expert systems and how forward chaining works.

• Fact 1: The patient has a fever.
• Fact 2: The patient has a cough.
• Rule: If a patient has a fever and a cough, then they may have the flu.
• New fact: The patient may have the flu.

Backward Chaining

Backward chaining is a goal-driven reasoning strategy that begins with a hypothesis or goal and works backward to determine which facts need to be true to support that goal. In this strategy, the system aims to prove or disprove the hypothesis by identifying the required facts and applying relevant rules.

Here’s how backward chaining works.

• Start with a goal

The system starts with a predefined goal or hypothesis. This is usually a conclusion the system is trying to prove.

• Search for supporting facts

The system explores the knowledge base to find rules that can support the goal.

• Check preconditions

For each rule, the system checks whether the necessary conditions (facts) are true. If the conditions aren't satisfied, the system attempts to find facts that lead to those conditions.

• Work backward

The process continues to work backward, recursively checking the conditions until it either confirms the goal or finds insufficient data to do so.

Also Read: How to Implement Machine Learning Steps: A Complete Guide

Curious to know more about an expert system in artificial intelligence? Check the subsequent section.

Expert System Technology in Artificial Intelligence

Expert system technology in AI provides the platforms, tools, and environments required for the development, deployment, and execution of expert systems. All these come together to replicate human expert decision-making in specific domains. 

Here’s the breakdown of the expert system in artificial intelligence environment.

Expert System Development Environment      

Here are the key components of an expert system development environment.

• Workstations & minicomputers

These components are used to run expert systems. Depending on the complexity, systems may run on high-performance workstations or smaller minicomputers.

• Programming languages

Experts favor languages like LISP and PROLOG for expert system development due to their strength in handling symbolic reasoning and logic.

• Large datasets

Expert systems rely on large datasets that are continuously updated to provide accurate, domain-specific insights.

Also Read: Top 10 Machine Learning Datasets Project Ideas For Beginners

Tools     

The development environment for expert systems needs specialized tools for building and maintaining systems. Here are some of the tools required for the purpose.

• Editors

Developers use text-based editors to write and edit rules, facts, and logic within the knowledge base.

• Debugging tools

Developers use these tools to trace the execution of the inference engine and correct errors in the logic or reasoning process.

• Multi-windows

Integrated development environments (IDEs) with multi-window support allow developers to simultaneously view code, rules, and debugging output, improving workflow.

Also Read: Top 35 Software Testing Projects to Boost Your Testing Skills and Career

Shells  

Shells are pre-built software frameworks that give the essential components for building expert systems, including inference engines, knowledge base management, and user interfaces. The Shells allow developers to focus more on domain knowledge than underlying architecture.      

Here are some of the popular shells used for the development of an expert system in artificial intelligence.                                                                                                        

• Java Expert System Shell (JESS)

A popular rule-based expert system shell that is used in Java. It provides an efficient environment for rule-based reasoning and is popular for building complex systems.

• Vidwan

This expert system shell is tailored to develop knowledge-based applications. It supports both forward and backward chaining with customizable knowledge-based management tools.

Also Read: Top 10 Artificial Intelligence Tools & Frameworks

After going through all the necessary tools for the system, let’s look at how an expert system in artificial intelligence differs from a conventional system and human experts.

Conventional System vs Human Expert vs Expert System in Artificial Intelligence

Although an expert system in artificial intelligence is designed to replicate human decision-making processes, it diverges from conventional systems in its methodology and ability to consistently arrive at the same conclusion for a given set of inputs.

Here’s a tabulated representation of how an expert system in artificial intelligence differs.

Also Read: Data Processing in Hadoop

Curious about how an expert system in artificial intelligence can tackle real-world challenges? Check out the section below.                                                                                                                                                     

What are the Real World Applications of Expert System in Artificial Intelligence?

Expert systems have found widespread use across different industries due to their ability to emulate the decision-making process of human experts. Due to their higher efficiency rates, they can improve the quality of decisions taken.

Here are the real-world application of expert system in artificial intelligence.  

MYCIN

Developed in the 1970s, MYCIN can diagnose infectious blood diseases, particularly bacterial infections, and recommend antibiotics. It takes patient data (such as fever and symptoms) and recommends antibiotic treatments.

R1/XCON

Developed in the 1980s by the Digital Equipment Corporation (DEC) as an expert system for configuring VAX computer systems. It selects the correct components for a VAX computer system based on user specifications, such as memory, processors, and peripherals.

PXDES

PXDES is mainly used in manufacturing industries to troubleshoot and identify malfunctions in complex machines. The system uses a knowledge base of common equipment failures and solutions to diagnose potential issues.

CaDet

CaDet (short for Causal Diagnosis Expert System) can diagnose causal relationships in chemical processes. It identifies potential causal relationships between variables in chemical plants and offers suggestions for engineers to take corrective action.

DXplain

DXplain can assist healthcare providers in diagnosing diseases based on symptoms, medical history, and lab results. Based on input symptoms and medical data, it generates a list of possible diagnoses along with reasoning.

DENDRAL

DENDRAL can analyze chemical structures and predict molecular structures based on mass spectrometry data. The system analyzes mass spectrometry data to deduce the structure of chemical compounds, especially organic molecules.

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Now that you understand the application of expert system, let's explore their strengths and weaknesses.

What are the Pros and Cons of Expert Systems in Artificial Intelligence?

An expert system in artificial intelligence offers valuable applications in modern technologies, but it also has notable advantages and limitations that should be carefully evaluated based on your specific use case.

Here are the advantages of an expert system in artificial intelligence.

• Availability and consistency in decision-making

Expert systems do not suffer from fatigue, emotional fluctuations, or bias, making them suitable for tasks where consistent, repeatable decisions are required.

• Can handle complex and specialized knowledge

Expert systems can process large volumes of domain-specific data and provide insight that is difficult for human experts to achieve on a consistent basis.

• Reduces human error and enhances efficiency

Automated decision-making capabilities of expert systems reduce human error caused by oversights or bias. This improves overall efficiency and ensures more accurate outcomes for critical tasks.

• Can be used in dangerous or remote environments

Expert systems are suitable for use in environments that are hazardous or difficult for humans to access, such as space exploration or deep-sea operations.

Here are some major limitations of an expert system in artificial intelligence. 

• Knowledge acquisition bottleneck

Building an accurate knowledge base for an expert system is time-consuming and resource-intensive. Domain experts have to codify their expertise into rules and data, which makes the process slow.

• Difficulty in handling vague or incomplete information

Expert systems are designed to work with structured inputs. They face difficulty making accurate decisions when faced with unclear or missing information.

• Lack of common sense reasoning

The expert system cannot adapt to new, unforeseen situations in the way humans can. If a problem falls outside the scope of the system’s knowledge, it may fail to provide a reasonable solution.

• Expensive to develop and maintain

The initial development and maintenance of the system consume lots of resources. The long-term cost of creating and maintaining expert systems in specialized domains is very high.

After understanding the benefits and drawbacks of expert systems in artificial intelligence, let's explore the career opportunities in this field.

How Can upGrad Help You Build a Career?

Mastering expert systems allows you to play a pivotal role in industries that rely heavily on precision, such as medical diagnostics, industrial automation, and scientific research.

To work with expert systems, you’ll require a strong foundation in AI, machine learning, knowledge engineering, and domain-specific expertise.

upGrad offers specialized programs designed to equip you with the knowledge and skills required for careers in AI and expert systems.

Here are some of the courses offered by upGrad in AI and machine learning.

• Introduction to Generative AI
• Fundamentals of Deep Learning and Neural Networks
• Artificial Intelligence in the Real World
• ChatGPT for Developers

Do you need help deciding which course to take to advance your career in AI and Machine Learning? Contact upGrad for personalized counseling, or visit your nearest upGrad career centre to start accelerating your future!

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References:
https://www.science.org/doi/10.1126/science.adn9602