Using an AI Expert System Instead of Machine Learning

Prior to starting an AI project, the first choice you need to make is whether to use an expert system (a rules based system) or machine learning. Basically the choice comes down to the amount of data, the variation in that data and whether you have a clear set of steps for extracting a solution from that data. An expert system is best when you have a sequential problem and there are finite steps to find a solution. Machine learning is best when you want to move beyond memorizing sequential steps, and you need to analyze large volumes of data to make predictions or to identify patterns that you may not even know would provide insight — that is, when your problem contains a certain level of uncertainty.

Examples of Expert Systems - Use Cases

Think about it in terms of an automated phone system.

Older phone systems are sort of like expert systems; a message tells the caller to press 1 for sales, 2 for customer service, 3 for technical support and 4 to speak to an operator. The system then routes the call to the proper department based on the number that the caller presses.

Newer, more advanced phone systems use natural language processing. When someone calls in, the message tells the caller to say what they’re calling about. A caller may say something like, “I’m having a problem with my Android smart phone,” and the system routes the call to technical support. If, instead, the caller said something like, “I want to upgrade my smartphone,” the system routes the call to sales.

The challenge with natural language processing is that what callers say and how they say it is uncertain. An angry caller may say something like “That smart phone I bought from you guys three days ago is a piece of junk.” You can see that this is a more complex problem. The automated phone system would need accurate speech recognition and then be able to infer the meaning of that statement so that it could direct the caller to the right department.

With an expert system, you would have to manually input all the possible statements and questions, and the system would still run into trouble when a caller mumbled or spoke with an accent or spoke in another language.

The other possibility: Machine learning (ML)

In this case, machine learning would be the better choice. With machine learning, the system would get smarter over time as it created its own patterns. If someone called in and said something like, “I hate my new smart phone and want to return it,” and they were routed to sales and then transferred to customer service, the system would know that the next time someone called and mentioned the word “return,” that call should be routed directly to customer service, not sales.

Conclusion - Artificial Intelligence (AI) with Human Expert View

When you start an AI program, consider which approach is best for your specific use case. If you can draw a decision tree or flow chart to describe a specific task the computer must perform based on limited inputs, then an expert system is probably the best choice. It may be easier to set up and deploy, saving you time, money and the headaches of dealing with more complex systems. If, however, you’re dealing with massive amounts of data and a system that must adapt to changing inputs, then machine learning is probably the best choice.

Some AI experts mix these two approaches. They use an expert system to define some constraints and then use machine learning to experiment with different answers. So you have three choices — an expert system, machine learning or a combination of the two.

Frequently Asked Questions

What are the advantages of expert systems over machine learning?

Expert systems provide a range of advantages, such as:

• Encapsulating the decision-making ability of a human expert

• Ensuring consistent results

• Easing documentation for decision processes

• Functioning well in well-defined, narrow domains without requiring vast datasets for training as machine learning does.

What is a knowledge-based system?

A knowledge-based system is a computer program that uses a repository of knowledge about a specific domain. It leverages this knowledge to solve complex problems by mimicking the decision-making process of human experts.

How does an expert system emulate the decision-making ability of a human expert?

An expert system emulates the decision-making ability of a human expert by using a knowledge base containing domain-specific information and an inference engine that applies logical rules to this information to make decisions or solve problems.

What is the role of the inference engine in an expert system?

The inference engine is a crucial component of an expert system that applies logical rules from the knowledge base to deduce new information or make decisions. It functions similarly to the human thinking process, enabling the expert system to solve problems and provide solutions.

Can you provide examples of expert systems used in various domains?

Examples of expert systems include MYCIN, used for medical diagnosis of bacterial infections, and DENDRAL, used for chemical analysis. These systems utilize domain-specific knowledge and expertise to emulate the decision-making process of human experts in their respective fields.

How do expert systems handle knowledge acquisition?

Knowledge acquisition in expert systems involves gathering information from multiple experts, converting it into a structured format, and integrating it into the knowledge base. This process is crucial for maintaining an expert system and ensuring it remains up-to-date with current expertise and practices.

What are the differences between forward chaining and backward chaining in expert systems?

Forward chaining starts with known facts and applies inference rules to extract more data until a goal is achieved. For example, it might deduce a diagnosis based on available symptoms. Backward chaining starts with a potential goal and works backward through inference rules to confirm whether the goal can be supported by known facts. It's often used to verify hypotheses in a logical manner.

How does an expert system's user interface function?

The user interface in an expert system allows users to input queries and interact with the system. It is designed to be user-friendly and enables the user to query the system, receive explanations for decisions, and visualize information from the knowledge base. This makes the system accessible to both experts and non-experts.

What is the role of domain experts in the development of expert systems?

Domain experts play a crucial role in the development of expert systems by providing the knowledge and experience in a particular field that the system needs to function .

They work with engineers to ensure that the knowledge base accurately reflects the decision-making processes involved in solving specific problems within the domain.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).

More sources

1. https://www.javatpoint.com/advantages-and-disadvantages-of-expert-system

2. https://en.wikipedia.org/wiki/Knowledge-based_systems

3. https://en.wikipedia.org/wiki/Expert_system

4. https://www.parascript.com/blog/machine-learning-ai-vs-expert-systems-ai/

5. https://www.techtarget.com/searchcio/definition/knowledge-based-systems-KBS

6. https://www.slideshare.net/slideshow/572-11293384/11293384

7. https://www.upgrad.com/blog/expert-system-in-artificial-intelligence/

8. https://www.indeed.com/career-advice/career-development/what-is-knowledge-based-system

9. https://www.sciencedirect.com/science/article/pii/0378720693900696

10. https://www.naukri.com/code360/library/expert-system-in-ai

11. https://es.wikipedia.org/wiki/Knowledge-based_systems

12. https://www.techtarget.com/searchenterpriseai/definition/expert-system

13. https://stackoverflow.co/teams/resources/knowledge-based-system/

14. https://helpjuice.com/blog/knowledge-based-systems

15. https://www.geeksforgeeks.org/expert-systems/

16.  https://www.sciencedirect.com/topics/computer-science/knowledge-based-systems