What is Inference Engine

Introduction to Inference Engines

An inference engine is a fundamental component of an expert system, which is a computer program that mimics the problem-solving capabilities of a human expert. Precisely, an inference engine is a software module that helps deduce conclusions from a set of facts or rules. It is responsible for carrying out the logical operations that are necessary for reaching inferences.

The inference engine processes the rules and data provided by the knowledge base and attempts to arrive at a desired conclusion that can help solve a problem. In simple terms, the inference engine is like the “brain” of an expert system that performs reasoning and decision-making tasks.

In this article, we’re going to take a closer look at inference engines, understanding what they are and how they work, as well as the different types of inference engines available today.

How do Inference Engines Work?

Inference engines use rules and facts to draw conclusions about a specific situation. These rules and facts can be explicitly programmed or learned by the system through various machine learning techniques. Regardless of how it gets the rules and facts, the inference engine uses these rules and facts to create a knowledge base.

The knowledge base is essentially a collection of all the facts and rules that the system has access to. Facts are simple pieces of information that are considered to be true. Rules, on the other hand, are statements about how different facts are related to each other.

Inference engines use a variety of logic to search through the knowledge base and make deductions about the information it contains. The most common form of logic used is the “if/then” logic which is used to express rules in the form of an “if” statement that describes the conditions under which the rule applies, and a “then” statement that describes the action the system should take if the condition is met.

Types of Inference Engines

There are different types of inference engines available today, each designed to perform a specific task or cater to specific domains. Some of the common types of inference engines include:

The forward chaining engine, also known as the data-driven engine, starts with a set of initial facts and rules and works forward through the chain of rules to reach the desired conclusion. It works by matching the conditions of a rule with the facts available in the knowledge base, and if all the conditions are satisfied, the rule is fired, and the result is added to the knowledge base.

This type of inference engine is useful when there are many interdependent rules and complex action sequences are required. It is commonly used in domains such as manufacturing, logistics, and transportation.

Backward chaining engine, also known as the goal-driven engine, starts with a problem and works backward to find a solution. It works by selecting the final goal and then searching for the rules that will help achieve that goal. It is particularly useful when there are many possible paths to reach the desired outcome.

The backward chaining engine is commonly used in diagnostic and troubleshooting systems, such as those used in medical diagnosis or software debugging.

The hybrid inference engine is a combination of both backward and forward chaining. It combines the best features of both engines, allowing it to handle a wide range of problems. It is particularly useful when the problem requires a mixed approach, such as in planning and scheduling.

Advantages and disadvantages of Inference Engines

• Automated decision-making: By using the inference engine, expert systems can make decisions faster and more accurately than humans.
• Reduced costs: Expert systems can help organizations reduce costs by minimizing errors and optimizing processes.
• Improved performance: Expert systems can achieve higher performance than humans because they can process information faster and more efficiently.
• Consistency: Inference engines ensure consistent decision-making across different users or scenarios.

• Limited domain expertise: Expert systems are only as good as the rules and facts that are provided to them. They are not capable of learning and adapting to situations outside of their domain expertise.
• Over-reliance on technology: Expert systems can lead to over-reliance on technology, which can cause problems if the system breaks down or fails to provide accurate results.
• Elaborate programming: Developing an expert system can be time-consuming and costly, as it requires the development of the knowledge base, rules, and inference engine.

Conclusion

Inference engines are an essential component of expert systems, providing reasoning and decision-making capabilities. They allow expert systems to analyze and process large amounts of data to arrive at optimal solutions to problems. While there are several types of inference engines available, they all work by using rules and facts to create a knowledge base and drawing conclusions based on that knowledge base.