Inference Engine in AI

Introduction

Inference Engine in AI is a component of the knowledge base. It's a tool used to make logical deductions about knowledge assets. Inference engines in Artificial Intelligence are useful in working with all sorts of information.

The inference engine helps stakeholders to get those analytical insights from the storehouse of information at their disposal.

For Example:

1. An inference engine may take certain facts from a knowledge base, maybe facts about where customers are located, what products customers have bought, or what transactions have taken place, and "infer" certain logical conclusions
2. To enhance Business Intelligence

What is an Inference Engine?

In artificial intelligence, the inference engine is a system component that applies logical rules to the knowledge base to deduce new information and to surface new facts and relationships. Implementation of inference engines can proceed via induction or deduction. The process of inferring relationships between entities utilizing machine learning, machine vision, and natural language processing has exponentially increased the scale and value of knowledge graphs and relational databases in recent years.

This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in two modes, either special rule or facts: forward chaining and backward chaining. Forward Chaining starts with the known facts and asserts new facts. Backward Chaining begins with goals and works backward to determine what facts must be asserted to achieve the goals.

Architecture of Inference Engine

Typically, IF-THEN rules represent the logic an inference engine uses. Such rules typically follow the format IF logical expression THEN logical expression. Before expert systems and inference engines, researchers in artificial intelligence focused on more powerful theorem prover environments that provided much more comprehensive first-order logic implementations. For instance, universal quantification (some statement is true for all X) and existential quantification (some X exists such that some statement is true) were examples of general statements. Researchers discovered that these theorem-proving environments had both advantages and disadvantages. In 1965, it was far too simple to construct logical expressions that could take an indeterminate amount of time to complete. For instance, statements over an infinite set, such as the set of all natural numbers, are common in universal quantification. Although such statements are perfectly reasonable and even required in mathematical proofs, including them in an automated theorem prover running on a computer could send it into an infinite loop. The developers still received a very powerful general mechanism to represent logic by focusing on IF-THEN statements—what logicians call modus ponens—but one that could be utilized effectively with computational resources. In addition, some psychological research indicates that humans tend to prefer IF-THEN representations when storing complex knowledge.

"If you are human, then you are mortal" is a straightforward illustration of modus ponens frequently used in introductory logic books. In pseudocode, this can be represented as:

Rule-1:

The following is a trivial illustration of how this rule might be applied in an inference engine: Human(x) => Mortal(x)

The inference engine would search the knowledge base for any facts that corresponded to Human(x), and for each such fact, it would add the new information Mortal(x) to the knowledge base. This process was known as forward chaining. Therefore, it would conclude that Socrates was mortal if they discovered a human-like object. The system would be given a goal in backward chainings, such as answering the question, "Is Socrates mortal?" It would go through the knowledge base to see if Socrates was human; if he were, it would say that he was also dead. However, integrating the inference engine with a user interface was common in backward Chaining. This way, the system could now be interactive rather than just automated. In this simple illustration, if the system were given the objective of determining whether Socrates was mortal but did not yet know whether he was human, it would produce a window asking the user, "Is Socrates human?" also would utilize that data as needs be.

The second early development of expert systems was brought about by this innovation, which involved integrating the inference engine with a user interface. Capabilities for an explanation. The unequivocal portrayal of information as rules instead of code made it conceivable to create clarifications for clients: the two clarifications continuously and sometime later. Thus, if the user were asked, "Is Socrates human?" by the system, The system would use the chain of rules to explain why it was attempting to ascertain that piece of knowledge: The user might wonder why she was being asked that question. Specifically, it must determine whether Socrates is human or mortal. At first, these explanations were similar to the usual debugging data developers work with when fixing any system. Utilizing natural language technology, on the other hand, to ask, comprehend, and generate questions and explanations using natural languages rather than computer formalisms was a thriving area of research.

A three-step cycle is followed by an inference engine: match rules, choose rules, and put rules into action. When the rules are followed, new facts or objectives will frequently be added to the knowledge base, which will repeat the cycle. This cycle continues until there are no more rules that can be used.

In the initial step, match manages, the deduction motor finds the guidelines set off by the ongoing items in the information base. The engine in forward Chainingchaining looks for rules whose antecedent (left side) matches a knowledge base fact. The engine searches for antecedents that can satisfy one of the current goals in backward Chaining.

The inference engine prioritizes the various matched rules in the second step, select rules, to determine their execution order. The engine iterates back to step one after executing each matched rule in the order determined in step two in the final step, execute rules. The cycle goes on until there are no more rules that match.

Examples of Inference Engine

1. Rule-based Production Systems
2. Artificial Intelligence
3. Expert Systems
4. Fuzzy Modelling
5. Data Science
6. Semantic Web
7. Declarative Network

1. Rule-based Production Systems

Three key components comprise a rule-based production system: working memory, a collection of rules, and an inference engine. Such a system's working memory serves as the central repository for all the information on the situation. The knowledge needed to solve the problem is included in the rules. The primary purpose of the inference engine is to apply the knowledge and information found in the set of rules to the information stored in working memory. The information from the rules and the working memory data are obtained for this reason. The matching process is then fed by the inference engine's combination of both to produce an inference output known as a conflict set. The inference engine runs an infinite loop of the match, resolves, and executes until the problem is solved.

2. Artificial Intelligence

Inference engines are used by artificial intelligence to gather all potential solutions to a given problem and assist the computer in selecting the best one. Generally speaking, an inference system is software used in artificial intelligence-enabled gadgets and devices to determine how to react to an input signal based on the knowledge base's facts.

3. Expert Systems

Expert systems are the main application for the inference engine. An expert system is a computer program that mimics human decision-making in some way. As an illustration, in the case of a knowledge-based expert system, the inference engine extracts data from the knowledge base, manipulates it, finds solutions to the input problem, and then selects the best course of action.

4. Fuzzy Modelling

In fuzzy modeling, an inference system is typically employed. It works well in cases where a precise conclusion or result has to be drawn from a large amount of approximative input data. There are a variety of fuzzy inference engines. However, the most frequently used ones include the product inference engine, root sum square inference engine, max-min inference engine, max product inference engine, etc.

5. Data Science

Data science also uses an inference method to analyze and glean valuable information. Structured, semi-structured, or unstructured data are all possible. Gaining an understanding of business and marketing data is made much easier with the aid of an inference system. It uses the customer's location, product choices, and needs as facts or input data. A collection of algorithms is then used to analyze the data to get a logical conclusion. Business people may benefit from this by increasing customer loyalty, product sales, or occasionally both.

6. Semantic Web

The semantic web makes extensive use of inference engines. The systematic organization of a data mesh into the semantic web makes it simple for machines to understand. It expresses data as a globally connected database and expands the current World Wide Web. The inference engines' algorithm and data manipulation significantly add new facts and knowledge to the pre-existing data.

7. Declarative Network

The Pega platform often carries out declarative processing using an inference engine and a declarative network. It facilitates independent examination of the network's dynamic features and simplifies the generated application.

Types of Inference Engine

As Expert Systems evolved, many new techniques were incorporated into various types of inference engines.

1. Truth Maintenance
2. Hypothetical Reasoning
3. Fuzzy Logic
4. Ontology Classification

1. Truth Maintenance

• Systems for maintaining truth keep track of dependencies in a knowledge base so that dependant knowledge may also be changed when facts are changed.
• For instance, the system will retract the claim that Aryan is mortal if it discovers that he is no longer recognized as a man.

2. Hypothetical Reasoning

• The knowledge base can be broken up into several viewpoints, or "worlds", in hypothetical reasoning.
• This enables the inference engine to investigate several options simultaneously.
• In this straightforward example, the system would wish to consider the effects of both claims, specifically what will happen if Aryan is a man and what will happen if he is not.

3. Fuzzy Logic

• One of the earliest modifications to the straightforward use of rules to describe knowledge was assigning a probability to each rule.
• So, instead of saying Aryan is mortal, say Aryan may be mortal with a certain likelihood.
• A mixture of probabilities and advanced methods for uncertain reasoning were used in certain systems to expand simple probabilities.

4. Ontology Classification

• A new kind of reasoning was made feasible with the knowledge base's inclusion of object classes. The system may also reason about the structure of the objects in addition to only the values of the objects.
• In this straightforward illustration, Man may stand in for an object class, and R1 can be described as the rule that establishes the category of all men.
• Classifiers is the name given to these sorts of special-purpose inference engines. Classifiers would be a critical technology for the Internet and the rising market even if they were not widely employed in expert systems. They are especially powerful for unstructured volatile domains.

Inference Engine Methods

Forward Chaining

Forward chaining is sometimes referred to as a forward deduction or forward reasoning method when implementing an inference engine. Applying inference rules (Modus Ponens) in the forward direction, forward chaining is a type of reasoning that commences with atomic sentences in the knowledge base and proceeds until the desired outcome is attained.

Starting with known facts, the Forward-chaining algorithm activates all rules whose premises are met and adds their conclusion to the known facts. Up till the issue is resolved, this process is repeated.

Properties of Forward-Chaining:

• It is a down-up approach, moving from bottom to top.
• It is a process of making a conclusion based on known facts or data by starting from the initial state and reaching the goal state.
• The forward-chaining strategy is also known as data-driven since we use the data to achieve the goal.
• Expert systems like CLIPS, business rules and production rule systems frequently employ the forward-chaining technique.

Backward Chaining

When implementing an inference engine, backward Chaining is sometimes referred to as a backward deduction or reasoning technique. Using a chain of rules to uncover known facts that support the aim, a backward chaining algorithm starts with the goal and works backward.

Properties of backward Chaining:

• It is known as a top-down approach.
• Backward Chainingchaining is based on the modus ponens inference rule.
• Backward chaining divides the objective into sub-goal or sub-goals to demonstrate the facts' integrity.
• It is a goal-driven method since a set of goals determines the rules chosen and applied.
• Game theory, automated theorem-proving tools, inference engines, proof helpers, and numerous AI applications use the backward chaining process.
• The backward chaining method mainly employed a depth-first search approach for proof.

Advantages and Disadvantages of Inference Engine

Advantages

1. Enhanced Output and Productivity
2. Reduce the Decision-Making Time
3. Enhance Process and Product Quality
4. Flexibility
5. Decrease the Downtime
6. Easier Equipment Operations
7. The Capture of Scarce Expertise
8. Removal of the Need for Expensive Equipment
9. Functioning in the Difficult Environment
10. Accessibility to Knowledge and Help Desks
11. Predictive Modelling Power

Disadvantages

1. Hard
2. Requirement of Expert Engineers
3. Limited Domain and Vocabulary
4. Different Thought, Costly and Dependent