- Artificial Intelligence Tutorial
- AI - Home
- AI - Overview
- AI - History & Evolution
- AI - Types
- AI - Terminology
- AI - Tools & Frameworks
- AI - Applications
- AI - Real Life Examples
- AI - Ethics & Bias
- AI - Challenges
- Branches in AI
- AI - Research Areas
- AI - Machine Learning
- AI - Natural Language Processing
- AI - Computer Vision
- AI - Robotics
- AI - Fuzzy Logic Systems
- AI - Neural Networks
- AI - Evolutionary Computation
- AI - Swarm Intelligence
- AI - Cognitive Computing
- Intelligent Systems in AI
- AI - Intelligent Systems
- AI - Components of Intelligent Systems
- AI - Types of Intelligent Systems
- Agents & Environment
- AI - Agents and Environments
- Problem Solving in AI
- AI - Popular Search Algorithms
- AI - Constraint Satisfaction
- AI - Constraint Satisfaction Problem
- AI - Formal Representation of CSPs
- AI - Types of CSPs
- AI - Methods for Solving CSPs
- AI - Real-World Examples of CSPs
- Knowledge in AI
- AI - Knowledge Based Agent
- AI - Knowledge Representation
- AI - Knowledge Representation Techniques
- AI - Propositional Logic
- AI - Rules of Inference
- AI - First-order Logic
- AI - Inference Rules in First Order Logic
- AI - Knowledge Engineering in FOL
- AI - Unification in First Order Logic (FOL)
- AI - Resolution in First Order Logic (FOL)
- AI - Forward Chaining and backward chaining
- AI - Backward Chaining vs Forward Chaining
- Expert Systems in AI
- AI - Expert Systems
- AI - Applications of Expert Systems
- AI - Advantages & Limitations of Expert Systems
- AI - Applications
- AI - Predictive Analytics
- AI - Personalized Customer Experiences
- AI - Manufacturing Industry
- AI - Healthcare Breakthroughs
- AI - Decision Making
- AI - Business
- AI - Banking
- AI - Autonomous Vehicles
- AI - Automotive Industry
- AI - Data Analytics
- AI - Marketing
AI - Advantages & Limitations of Expert Systems
An Expert System is a computer program that mimics human decision-making in a specific area. Similar to a human, it resolves problems, provides suggestions, and makes decisions based on a knowledge base and rules of logic.
Expert systems analyze information and make decisions based on pre-established rules and logic they do not think like humans.
They are often utilized to assist professionals in resolving complex issues in manufacturing, finance, law, and medicine.
NASA uses expert systems on space missions to assist astronauts and control autonomous spacecraft. During the Deep Space 1 mission, they employed an expert system called Remote Agent. This system had an impact on diagnosing system problems making real-time decisions, and adjusting navigation without human involvement. This AI-based system reduced the need for constant human oversight by allowing the spacecraft to operate on its own in deep space.
Benefits of Expert Systems
Following are the advantages of Expert Systems −
Availability: They are easily available due to mass production of software.
Making Decisions Consistently: Expert systems remove human inconsistencies caused by emotions or fatigue by arriving at consistent, objective decisions.
Less Production Cost: Production cost is reasonable. This makes them affordable.
Knowledge Preservation− These methods make sure that crucial information is not lost as a result of employee retirements or turnover by keeping specialized knowledge.
Speed: They offer great speed. They reduce the amount of work an individual puts in.
Scalability: Once developed, expert systems can be easily expanded and deployed to support multiple users across various locations simultaneously.
Less Error Rate: Error rate is low as compared to human errors.
Education: They shorten the learning curve in specialized areas by offering high-quality coaching to newcomers.
Reducing Risk: They can work in the environment dangerous to humans.
Steady response: They work steadily without getting motional, tensed or fatigued.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −
Lack of Common Sense: Expert systems lack human intuition and general knowledge they just follow pre-established rules and facts which are stored in knowledge base.
Incapacity to Learn on Their Own: In contrast to humans who learn from experience, they cannot develop or change unless specifically revised.
High Development Cost and Time: Developing an expert system requires a lot of programming, knowledge acquisition, and maintenance.
Limited to Specific Areas: They work well in certain areas and work badly when applied to more general or unrelated problems.
Relying on engineers: with expertise is essential for updating or enhancing an expert system, which can make implementing changes quite difficult.
Challenges with Missing or Uncertain Data: Expert systems often struggle to make conclusions when they encounter missing or ambiguous information.
Lack of emotional intelligence: limits their use in some situations because they are unable to understand empathy, human feelings, or human interaction such as in customer support. For example, expert systems may struggle to handle sensitive situations in customer service where a human touch, empathy, and comprehension are needed.