# AI - Expert Systems

## Summary
* [Overview](#overview)
* [How does Expert System work?](#how-does-expert-system-work?)
* [Components of an Expert System](#components-of-an-expert-system)
* [Application of Expert System](#application-of-expert-system)
* [Examples of expert system](#examples-of-expert-system)
* [Advantages and Disadvantages of Expert Systems](#advantages-and-disadvantages-of-expert-systems)
* [Conclusion](#conclusion)

## Overview
An experts system is a computer program designed to solve complex problems and to provide decision-making ability like a human expert.
The first expert system was developed in the year 1970, which was the first successful approach of artificial intelligence. It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base.

## How does Expert System work?
Modern expert systems use machine learning and artificial intelligence to mimic the judgment of real-world experts. These systems can improve their performance over time, learning and refining their abilities just like humans do with experience.
Expert systems accumulate experience and facts in a knowledge base and integrate them with an inference or rules engine, a set of rules that acts like a brain, allowing the system to apply this knowledge to solve problems presented to it.

## Components of an Expert System

These are three main components of an expert system:

- **User interface:** This is a part of expert system that can help a non-expert user to communicate with the expert system to find a solution.
- **Inference engine:**
The inference engine acts like the brain of the expert system. It's a rule-based system that maps known information from the knowledge base to a set of rules and makes decisions based on those inputs.
The inference engine uses one of two main approaches:

**1. Forward Chaining:** Forward chaining starts with facts and uses them to predict what might happen next.

**2. Backward Chaining:** Backward chaining starts with a situation and using the knowledge base to identify the cause.

- **Knowledge base:** The knowledge base is the foundation of an expert system. Human experts provide facts about the expert system's particular domain or subject area are provided that are organized in the knowledge base.
The knowledge base often includes a built-in knowledge acquisition module, like a smart assistant, that can constantly gather new information from external sources and store it in the knowledge base.

## Application of Expert System
Expert systems have a wide range of applications across various industries, including:
- Medical Diagnosis: Medical consultation support, Treatment recommendations
- Financial Planning
- Technical Troubleshooting: Equipment diagnostics, Repair recommendations
- Customer Service: Virtual assistants, Product recommendations
- Other Applications: Scientific research, Human resource management

## Examples of expert system
- **CaDet (Cancer Decision Support Tool)** is used to identify cancer in its earliest stages.
- **DENDRAL** helps chemists identify unknown organic molecules.
- **DXplain** is a clinical support system that diagnoses various diseases.
- **MYCIN** identifies bacteria such as bacteremia and meningitis, and recommends antibiotics and dosages.
- **PXDES** determines the type and severity of lung cancer a person has.
- **R1/XCON** is an early manufacturing expert system that automatically selects and orders computer components based on customer specifications.

## Advantages and Disadvantages of Expert Systems
### Advantages of Expert Systems

* **Low Accessibility Cost**: Expert systems provide a cost-effective means to access specialized knowledge and expertise. They allow users to benefit from expert-level insights without the need for direct consultation, which can be expensive and time-consuming.
* **Fast Response**: Expert systems offer prompt responses and recommendations. They can quickly analyze data and apply domain-specific knowledge to provide timely solutions, helping users make informed decisions and take immediate actions.
* **Emotion-Free Decision Making**: Unlike humans, expert systems are not influenced by emotions or biases. They make rational and objective decisions based solely on the encoded knowledge and rules, ensuring consistency and avoiding subjective judgments.
* **Low Error Rate**: Expert systems exhibit a low error rate in their decision-making process. By utilizing precise rules and heuristics, they minimize the chances of mistakes or oversight that may occur in human decision-making, leading to more accurate and reliable outcomes.
* **Explanation of Solutions**: Expert systems are capable of explaining how they arrived at a particular solution or recommendation. They can provide transparent explanations, offering insights into the reasoning and logic behind their decisions, which enhances user understanding and trust.

### Disadvantages of Expert Systems

* **Lack of Emotions**: Expert systems lack human emotions, which can be advantageous in some cases but may limit their ability to understand and respond to emotional or subjective aspects of certain problems. Emotional intelligence and intuition are areas where expert systems fall short.
* **Challenge of Common Sense**: Expert systems often struggle with common sense reasoning. They rely on explicitly encoded knowledge and rules, which may not capture the deep understanding of everyday situations or context-dependent reasoning that humans possess naturally.
* **Domain-Specific Development**: Expert systems are developed for specific domains or problem areas. Adapting them to new domains requires significant effort and expertise to capture and encode the relevant knowledge accurately. This domain specificity limits their flexibility and applicability across diverse fields.
* **Manual Updates Required**: Expert systems typically require manual updates to incorporate new knowledge or adapt to changing circumstances. They do not possess self-learning capabilities, necessitating human intervention to keep the knowledge base up to date, which can be time-consuming and resource-intensive.
* **Limited Explanation of Decision Logic**: Although expert systems can explain their solutions and recommendations, they may not provide a detailed explanation of the underlying decision logic. The reasoning process may involve complex interactions between rules and heuristics, making it challenging to trace and explain every step of the decision-making process.

## Conclusion

In conclusion, expert systems play a crucial role in addressing complex problems and are highly reliable and interactive. They find applications in various domains, including human resources, medicine, and more. The key advantages of expert systems in AI include improved decision quality, cost savings, consistency, speed, and reliability. These systems empower users with accurate and timely solutions, leading to enhanced efficiency and dependable outcomes.