Week 4-6: Types of Expert Systems (Classifications [Rule-Based and Fuzzy Logic])

Week 4-6: Types of Expert Systems (Classifications [Rule-Based and Fuzzy Logic])

These are a crucial subset of artificial intelligence (AI) that simulate the decision-making ability of a human expert.

Expert systems

These systems use a knowledge base filled with domain-specific information and rules to interpret and solve complex problems.

Expert systems

ENUMERATE: Types of Expert Sytems

• Rule-based Expert Systems

• Fuzzy Logic Expert Systems

• Frame-Based Expert Systems

• Neural Network-Based Expert Systems

• Neuro-Fuzzy Expert Systems

_________ expert systems are the most common type of expert system. They use a set of rules to reason about a problem and provide solutions or recommendations. These rules are created by human experts and are organized in a knowledge base

Rule-based

The concept of rule-based systems in artificial intelligence can be traced back to the _____, when researchers sought to replicate human decision-making processes.

1970s

The earliest AI systems were built on ______, inspired by how experts in various fields, such as medicine and law, used their knowledge to make decisions.

logical rules

ENUMERATE: Components of a Rule-Based System

• Rules

• Knowledge Base

• Inference Engine

• Working Memory

• User Interface

A foundational technology in artificial intelligence (AI), have long been instrumental in decision-making and problem-solving across various domains.

Rule-based systems

It operates using a set of predefined rules (if- then conditions) to process information and make decisions. It follows a structured approach that mimics human expert reasoning

Rule-based systems

The core of the system, these are conditional statements that define the system's behavior.

Rules

A rule generally follows the format _________.

"IF condition THEN action."

This is the repository where all the rules and facts are stored.

Knowledge Base

The ________ is built from domain-specific knowledge and can be manually curated or derived from expert input.

knowledge base

Contains a collection of if-then rules that define decision-making logic.

Knowledge Base

The _________ is the component that applies the rules to the knowledge base to derive conclusions or make decisions.

inference engine

The core of the system that applies rules to given inputs and derives conclusions.

Inference Engine

ENUMERATE: Inference Engine can work in two ways, what are these strategies?

• Forward Chaining

• Backward Chaining

Starts from known facts and applies rules to infer new conclusions.

Forward Chaining

Starts with a goal and works backward to find supporting rules.

Backward Chaining

TRUE OR FALSE

Forward chaining is goal-driven.

FALSE

Forward chaining is data-driven.

TRUE OR FALSE

Backward chaining is data-driven.

FALSE

Backward chaining is goal-driven.

It interprets the rules, processes them against the current facts or data, and determines the appropriate actions or outputs.

Inference Engine

This is a dynamic component that holds the current facts or data being processed by the system.

Working Memory

Stores dynamic data or facts that the system evaluates against rules.

Working Memory

Working Memory is also known as?

Facts Database

ENUMERATE: How the Rule-Based System Processes Information

• Input Collection / Data Input

• Rule Matching

• Rule Execution

• Conflict Resolution

• Output Generation / Feedback or Action

It is updated as the inference engine applies rules and new information becomes available.

Working Memory

n many rule-based systems, this allows users to interact with the system, input data, and receive outputs or recommendations.

User Interface

This step in rule-based system involves gathering facts from user input or a database. The system receives input data from the user or another source. This data can range from simple numerical values to complex information like patient symptoms or transaction records.

Input Collection / Data Input

This step in rule-based system involves the inference engine checking which rules apply to the given data. The inference engine examines the input data against the rules stored in the knowledge base. It looks for rules whose conditions match the input data.

Rule Matching

This step in rule-based system involves the system executing the matching rule and provides an output or action. Once a rule is matched, the inference engine executes the corresponding action. This might involve updating the working memory, deriving new facts, or generating an output

Rule Execution (Decision Making)

This step in rule-based system involves in cases where multiple rules are triggered simultaneously, the inference engine uses these strategies to determine which rule to apply first.

Conflict Resolution

ENUMERATE: Conflict Resolution Strategies

Prioritizing rules based on

• Specificity

• Order of entry

This step in rule-based system involves providing results or suggesting next steps. The system generates an output based on the executed rules. This output can be a decision, recommendation, or another form of response. For example, in a medical diagnosis system, the output might be a suggested treatment plan.

Feedback / Action or Output Generation

ENUMERATE: Types of Rule-Based Systems

• Forward Chaining Systems

• Backward Chaining Systems

• Hybrid Systems

These systems start with the available data and apply rules to infer new data until a goal is reached.

Forward chaining systems

These systems are often used in problem-solving and diagnostic systems.

Forward chaining systems

These systems start with a goal and work backward to determine which rules and data can achieve that goal.

Backward chaining systems

These systems are commonly used in expert systems where the goal is to reach a specific diagnosis or conclusion.

Backward Chaining Systems

Some systems combine forward and backward chaining to leverage the strengths of both approaches. These are useful in complex scenarios where both data-driven and goal-driven reasoning are required.

Hybrid systems

Advantages of Rules-based Systems

• Explainable Decisions – Easy to understand and debug.

• Consistency – Rules ensure uniform decision-making.

• No Need for Large Data – Works without machine learning training.

Limitations of Rules-based Systems

• Not Adaptive – Cannot learn or improve over time like AI.

• Complex Maintenance – Adding more rules makes the system harder to manage.

• Scalability Issues – Works best for structured problems but struggles with uncertainty.

Real-World Applications of Rule-Based Systems

• Education – Automated student evaluation and tutoring systems.

• Healthcare – Medical diagnosis systems using symptom-based rules.

• Finance – Fraud detection by flagging suspicious transactions.

• Cybersecurity – Identifying security breaches using predefined attack rules.

• Chatbots & AI Assistants – Rule-based responses in customer service

Rules-Based vs AI-Based Systems

Fuzzy logic expert systems use _______ to handle uncertainty and imprecision in data.

fuzzy logic

It is a mathematical framework that allows for degrees of truth instead of the traditional binary (true or false)

Fuzzy logic

These are used in product recommendation systems and image recognition applications.

Fuzzy Logic Systems

This is a method of reasoning that resembles human reasoning.

Fuzzy Logic (FL)

The approach of this logic imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.

Fuzzy Logic

NOTES:

Fuzzy logic is useful for commercial and practical purposes.

• It can control machines and consumer products.

• It may not give accurate reasoning, but acceptable reasoning.

• Fuzzy logic helps to deal with the uncertainty in engineering.

NOTES:

Fuzzy logic is useful for commercial and practical purposes.

• It can control machines and consumer products.

• It may not give accurate reasoning, but acceptable reasoning.

• Fuzzy logic helps to deal with the uncertainty in engineering.

ENUMERATE: Architecture of Fuzzy Logic System (Four Main Components)

• Fuzzification Module

• Knowledge Base (Rule Base + Database)

• Inference Engine

• Defuzzification Module

It transforms the system inputs, which are crisp numbers, into fuzzy sets.

Fuzzification Module

Converts crisp (numerical) inputs into fuzzy values (linguistic terms like "High," "Low," "Medium").

Fuzzification Module

Steps for Fuzzification Module

• Take precise numerical inputs.

• Convert them into fuzzy sets using membership functions.

• Assign a degree of membership (between 0 and 1) to each fuzzy set.

Stores fuzzy rules and membership functions for decision-making.

Knowledge Base (Rule Base + Database)

ENUMERATE: Component of Knowledge Base of Fuzzy Logic Systems

• Rule Base

• Database

In fuzzy logic systems, this contains IF-THEN rules for inference.

Rule Base

In fuzzy logic systems, stores membership functions used for fuzzification.

Database

Processes fuzzy inputs using the rule base to generate fuzzy outputs.

Inference Engine

It simulates the human reasoning process by making fuzzy inference on the inputs and IF-THEN rules

Inference Engine

Type of Inference Methods in Fuzzy Logic System

• Mamdani Inference Method

• Sugeno Inference Method

An inference method that uses fuzzy IF-THEN rules to generate a fuzzy output.

Mamdani Inference Method

An inference method that uses mathematical functions instead of fuzzy sets for output.

Sugeno Inference Method

Converts fuzzy output values into a crisp (numerical) value for real-world interpretation.

Defuzzification Module

It transforms the fuzzy set obtained by the inference engine into a crisp value.

Defuzzification Module

ENUMERATE: Common Defuzzification Methods

• Centroid Method

• Mean of Maximum (MoM)

• Max Criterion

It is the most commonly used defuzzification method.

Centroid Method

This defuzzification method averages the maximum membership values.

Mean of Maximum (MoM)

This defuzzification method takes the highest output value.

Max Criterion

Advantages of Fuzzy Logic Systems

• Handles Uncertainty – Works well with vague and imprecise data.

• Mimics Human Reasoning – Uses linguistic rules instead of strict conditions.

• Simple Implementation – No need for complex mathematical models.

• Can Be Integrated with AI & ML – Works well with neural networks.

Applications of Fuzzy Logic

• Education – Intelligent tutoring systems, student assessment.

• Healthcare – Medical diagnosis (e.g., disease risk levels).

• Robotics – Control systems for autonomous vehicles.

• Industrial Automation – Process control in manufacturing.

• Finance – Risk assessment models.

Advantages of FLSs

• Mathematical concepts within fuzzy reasoning are very simple.

• You can modify a FLS by just adding or deleting rules due to flexibility of fuzzy logic.

• Fuzzy logic Systems can take imprecise, distorted, noisy input information.

• FLSs are easy to construct and understand.

• Fuzzy logic is a solution to complex problems in all fields of life, including medicine, as it resembles human reasoning and decision making.

Disadvantages of FLSs

• There is no systematic approach to fuzzy system designing.

• They are understandable only when simple.

• They are suitable for the problems which do not need high accuracy.