Expert Systems Overview and Applications

A computer program that emulates the decision-making and problem-solving skills of a human expert in a specific field.

A branch of computer science focused on creating machines that can think and behave like humans.

It's the branch of computer science responsible for understanding how humans think and process information.

ES uses 'If-Then' rules to solve problems. These rules are based on the knowledge of experts in a specific area.

ES are designed to solve complex problems by using knowledge and rules similar to how human experts would.

ES are used in various fields, such as medicine, finance, and engineering, to provide expert advice and solutions.

ES aim to take the experience of human experts and transfer it into a computer system.

Knowledge Representation is how information from experts is organized and stored within an ES. This includes facts, rules, and relationships.

Rules of thumb or shortcuts based on experience, used to solve problems but not guaranteed to work every time.

A collection of facts, rules, and relationships organized and stored within an ES to represent expert knowledge.

A software component that interprets and applies the knowledge stored in the knowledge base to solve problems.

The person who interacts with the expert system, providing information and receiving solutions.

Expert systems are useful for solving complex problems that require specialized knowledge.

The main goal is to capture and transfer the knowledge and expertise of human experts into a computer system.

A component of an expert system that serves as the point of interaction between the system and its users, both human and non-human.

An expert system's ability to explain its reasoning, providing transparency into how decisions are made.

The process of designing and developing an expert system, involving gathering knowledge from experts and translating it into a computer-understandable format.

The collection of facts, rules, and relationships that represent an expert's knowledge in a specific domain.

Computer systems that emulate the decision-making and problem-solving abilities of human experts in a specific field.

Expert systems use structured knowledge bases to reach expert-level decisions, unlike conventional systems that rely on predefined algorithms.

Expert systems can adapt to new information by incorporating it into their knowledge base, while conventional systems require reprogramming for changes.

The ability of expert systems to provide clear explanations for their decisions, making them transparent and accountable.

A collection of facts and rules that describe all the knowledge about a specific problem area.

The interface through which users can interact with the expert system, providing input and receiving output.

A feature that explains the system's reasoning process to the user, enhancing transparency and trust.

A module responsible for acquiring and updating the knowledge base. It interacts with human experts to gather new knowledge and refine existing rules.

A specific area of application for an expert system, like medical diagnosis, financial forecasting, or technical troubleshooting.

The representation of knowledge in a rule-based expert system using IF (condition) THEN (action) structure.

When the condition part of a rule is satisfied, the rule is triggered, and the action part is executed.

Gathers expert knowledge to build an intelligent system.

Experts might struggle to clearly articulate their problem-solving processes.

Converting knowledge into a computer-understandable format.

Rules used in expert systems, often in the format 'IF condition THEN action'.

Structured way to represent objects and their attributes in expert systems.

Graphical knowledge representation where nodes are concepts and edges are relationships.

Evaluating an expert system's performance, ensuring it acts correctly and generalizes to new problems.

Ensuring an expert system makes accurate decisions aligned with expert opinions or real-world scenarios.

A structured way of representing knowledge about objects or concepts using frames with slots for attributes and values.

Data structures that represent knowledge about objects or concepts. Each frame has slots for attributes and values.

Attributes of an object or concept stored within a frame. Each slot has a value describing a specific attribute.

The identifier for a frame, specifying its specific knowledge domain (e.g., 'Car', 'Person', 'Computer').

Frame systems consist of multiple interconnected frames, allowing them to represent domain knowledge.

Frames capture static knowledge about objects or situations, enabling the system to retrieve information in a structured and meaningful way.

Frames allow for inheritance, where frames can inherit attributes from other frames.

Frame-based systems excel at handling expectations and responding effectively when those expectations are not met, unlike rule-based systems.