Challenges of Developing Expert Systems

What are the Challenges of Expert Systems?

Expert systems are complex, artificial intelligence programs designed to contain a high degree of knowledge and imitate the decision-making skills of human experts. Despite the fact that expert systems are highly advanced, they still face some challenges that greatly limit their effectiveness. Here is a look at some of the common challenges of expert systems.

Outdated Knowledge

The primary challenge associated with expert systems is keeping their knowledge up to date. Expert systems are built on the assumption that the knowledge they contain is accurate. However, as time changes, so does industry trends. Without regular updates, an expert system could quickly become outdated and unable to provide accurate information.

Complexity

Another issue with expert systems is their complexity. Because they include large amounts of data, the rules and logic implemented in the system can be difficult to understand and modify. This makes it difficult for the systems to adapt to changing environments or new data.

High Cost

Expert systems are also expensive to design and maintain. First, the costs associated with compiling and verifying the knowledge needed to build the system and then the cost of maintaining and updating the system, can be quite high. This is especially true if the system contains a large amount of data or its various components are spread over different locations.

Limitations

Finally, expert systems have certain limitations when it comes to flexibility. For example, the parameters or criteria for decision-making are usually fixed in advance, which means that if the conditions change, the system will not be able to adjust accordingly. Additionally, most expert systems lack the ability to “learn” from past mistakes or issues, meaning any errors made by the system cannot be corrected. Overall, expert systems are incredibly powerful tools but they still face a number of challenges. Keeping the knowledge current, managing complexity, reducing cost, and overcoming the limitations of the systems are all factors that must be taken into consideration when building and using expert systems.