[SBP INFORMATIKA UNINDRA] Grup 8 x S7C * Representasi Pengetahuan

Zahra Fitri

15 Oct 202309:14

Summary

TLDRThe video script discusses knowledge representation in expert systems, focusing on its definition, purpose, and types. It explains that knowledge representation is essential for capturing problem characteristics and making information accessible to problem-solving procedures. The script covers various representation methods, including semantic networks, frames, decision tables, and scripts. It also touches on logic-based representation and the use of deduction and induction in problem-solving. The presentation aims to provide a comprehensive understanding of how knowledge is structured and utilized within artificial intelligence systems.

Takeaways

📚 Knowledge Representation is a method used to encode knowledge within an expert system based on knowledge, aimed at capturing important problem characteristics and making the information accessible to problem-solving procedures.
🌐 Types of Knowledge Representation include Network, Structured Networks, Logic, Semantic Networks, Frames, and Decision Tables.
🔍 Network Representation captures knowledge as a graph where nodes represent objects or concepts of the problem, and edges represent relationships or associations.
📊 Structured Networks expand on the basic network by making nodes complex data structures, such as frames, logic, neural networks, and genetic algorithms.
🧠 Logic Representation involves the use of deductive and inductive reasoning processes that are compatible with symbolic or mathematical manipulation by computers.
🔗 Semantic Networks graphically represent knowledge, showing hierarchical relationships among basic components of objects or concepts.
🖼️ Frame Representation is a more complex way to store objects and attribute values, allowing objects to inherit values from other objects, unlike Semantic Networks.
📈 Decision Tables are used to solve complex logic within algorithmic programs, containing multi-level decisions that are difficult to represent directly with structured English or pseudo code.
🌳 Decision Trees are an example of a structured representation that graphically represents decision-making processes based on symptoms or conditions.
🎓 The script also includes an example scenario of a student exam process, illustrating the use of scripts and frames to represent knowledge about events and actions.

Q & A

What is the definition of knowledge representation as mentioned in the script?
-Knowledge representation is a method used to encode knowledge in an expert system that is based on knowledge. It is intended to capture important characteristics of a problem and make that information accessible by problem-solving procedures.
What is the purpose of knowledge representation?
-The purpose of knowledge representation is to capture the important features of a problem and make the information accessible by problem-solving procedures, enabling programmers to express the knowledge required to find solutions to problems.
What are the types of knowledge representation mentioned in the script?
-The script mentions several types of knowledge representation including Network, Structured Network, Logic, Semantic Network, Frame, Decision Table, and Script.
How does a Network representation capture knowledge?
-A Network representation captures knowledge as a graph where nodes represent objects or concepts of the problem faced, while edges represent the relationships or associations between nodes.
What is the difference between a Semantic Network and a Structured Network?
-A Semantic Network represents knowledge in a graphical form showing the hierarchical relationships of objects, while a Structured Network expands on this by making nodes into complex data structures.
What is Logic representation and how does it relate to computer reasoning?
-Logic representation is a scientific study of a series of reasoning, principles, and procedures that assist in the reasoning process. It involves the use of deductive and inductive reasoning in a form suitable for computer manipulation, such as symbolic logic.
Can you explain the Frame representation in the context of the script?
-A Frame is a more complex way to store objects and attribute values compared to a Semantic Network. It adds intelligence to the data representation, allowing objects to inherit values from other objects.
How does a Decision Table represent knowledge?
-A Decision Table is used as an aid to solve logic within an algorithmic program. It contains multi-level decisions and is structured with four main parts: condition ST, condition entry, action stop, and action entry.
What is a Script representation and how does it depict knowledge?
-A Script representation depicts knowledge based on known characteristics as experiences, describing objects and the sequence of events. It uses slots containing information about objects and actions that occur in a certain sequence.
What is an example of a Script representation provided in the script?
-An example of a Script representation given in the script is the preparation and conduct of an examination, detailing the steps from preparing exam papers to collecting and checking answer sheets.
What are the limitations of knowledge representation as discussed in the script?
-The script implies that knowledge representation should be clear, comprehensive, and efficient in displaying the existing natural limitations. It should make things transparent and complete.

Outlines

00:00

📚 Introduction to Knowledge Representation

The speaker begins by introducing themselves and their team, Del Group, comprising Zahra Fitri as the presenter, Mailiana as the reference finder, and Reza Ramadan as the PPT compiler. They delve into the concept of knowledge representation, defining it as a method used to encode knowledge within an expert system based on knowledge. The purpose of knowledge representation is to capture the essential characteristics of a problem and make this information accessible to problem-solving procedures. The presentation covers the objectives and types of knowledge representation, including capturing important problem features and enabling programmers to express the knowledge needed to solve problems. Various representation models are discussed, such as Network, Structured Network, Semantic Network, Frame, Decision Table, and Script.

05:03

🔍 Deep Dive into Knowledge Representation Models

This paragraph provides a detailed explanation of different knowledge representation models. It starts with the Semantic Network, which graphically represents knowledge showing hierarchical relationships among basic components. The paragraph then moves on to describe the Frame model, which is more complex than the Semantic Network and allows objects to inherit values from other objects. The Decision Table model is also discussed, which is used to solve logic within algorithmic programs and contains multi-level decisions. The Decision Table consists of four main parts: condition ST, condition entry, action stop, and action entry. Each condition has two possibilities: being met (symbolized by 'y') or not met (symbolized by 'n'). The paragraph also includes an example of a Decision Table for a tennis game, considering factors like Outlook, Temperature, Humidity, and Windy. Lastly, the Script model is introduced, which represents knowledge based on known characteristics and experiences, and is used to describe sequences of events.

Mindmap

Keywords

💡Knowledge Representation

Knowledge representation is a method used to encode knowledge within an expert system that is based on knowledge. It is designed to capture important characteristics of problems and make that information accessible by problem-solving procedures. In the context of the video, knowledge representation is central to the discussion on how to effectively structure and store expert knowledge for use in artificial intelligence systems. The script mentions various forms of representation such as networks, structured representations, and decision tables, each serving to capture different aspects of expert knowledge.

💡Expert System

An expert system is a computer program that mimics the decision-making ability of a human expert. It uses knowledge representation to solve complex problems that require expertise in a particular domain. The video script discusses expert systems in the context of knowledge-based systems, emphasizing the importance of knowledge representation in capturing the expertise needed to solve problems within these systems.

💡Network Representation

Network representation captures knowledge as a graph where nodes represent objects or concepts of the problem faced, and edges represent the relationships or associations between nodes. This concept is used in the video to illustrate how complex problem-solving knowledge can be structured in a visual and interconnected manner, allowing for the representation of relationships and flows within a system.

💡Structured Representation

Structured representation expands on the network by making nodes into complex data structures. This allows for a more sophisticated and detailed representation of knowledge, going beyond simple nodes and edges to include attributes and methods associated with the nodes. The video script uses this concept to show how knowledge can be organized in a way that is both comprehensive and accessible to problem-solving algorithms.

💡Semantic Network

A semantic network is a graphical representation of knowledge that shows the hierarchical relationships between basic components or objects. Nodes represent concepts or situations, and links represent the relationships between them. The video script mentions semantic networks as a way to visually represent complex knowledge structures, making it easier to understand the interconnections and dependencies within a knowledge base.

💡Logic Representation

Logic representation involves the use of logical statements and reasoning processes to represent knowledge. It includes both deductive and inductive reasoning, which are fundamental to the operation of expert systems. The video script discusses how logic is used to structure knowledge into a form that can be manipulated by computers, often starting from general premises to specific conclusions (deductive) or from specific instances to general rules (inductive).

💡Frame Representation

Frame representation is a more complex way to store objects and attribute values compared to semantic networks. It adds intelligence to the representation by allowing objects to inherit values from other objects. The video script uses the concept of frames to show how knowledge can be organized into structured records or objects with defined properties and behaviors, which is essential for building intelligent systems that can reason about complex data.

💡Decision Table

A decision table is a tool used to solve logic within algorithmic programs. It contains a series of decisions that are difficult to represent directly with structured English or pseudocode. The video script provides an example of a decision table for a tennis play scenario, illustrating how conditions and actions can be systematically organized to guide decision-making processes in expert systems.

💡Decision Tree

A decision tree is a graphical representation of the decision-making process. It starts with a problem and branches out into possible solutions or further questions. The video script mentions decision trees as a way to structure diagnostic or classification knowledge, allowing for a clear and logical flow from symptoms to conclusions.

💡Script Representation

Script representation organizes knowledge based on known characteristics or experiences, often representing sequences of events. The video script uses the example of a script for a student exam scenario, showing how scripts can capture the sequence of actions and conditions that define a particular situation or process within an expert system.

💡Transparency and Efficiency

The terms 'transparency' and 'efficiency' are used in the context of discussing the goals of knowledge representation. Transparency refers to the clarity with which knowledge is represented, making it understandable and verifiable. Efficiency refers to the effectiveness with which knowledge can be used to solve problems. The video script emphasizes the importance of these qualities in the design of knowledge representation systems, ensuring that they are both understandable and practical for problem-solving.

Highlights

Definition of knowledge representation as a method used to encode knowledge in an expert system.

Purpose of knowledge representation is to capture important problem characteristics and make information accessible to problem-solving procedures.

Knowledge representation as an expansion and visualization of expert knowledge for storage in a knowledge base.

Types of knowledge representation include Network, Structured Network, Logic, Semantic Network, Frame, Decision Table, and Script.

Network representation captures knowledge as a graph with nodes representing objects or concepts and edges representing relationships.

Structured Network expands on Network representation by making nodes complex data structures.

Logic representation involves deductive and inductive reasoning processes suitable for computer manipulation.

Semantic Network graphically represents knowledge showing hierarchical relationships between basic components.

Frame representation stores objects and attribute values, adding intelligence to data representation.

Decision Table is a tool for solving logic in algorithms containing multi-level decisions.

Script representation presents knowledge based on known characteristics as experiences or sequences of events.

Decision Table consists of four main parts: Condition ST, Condition Entry, Action Stop, and Action Entry.

Example of a Decision Table for a tennis game with conditions like Outlook, Temperature, Humidity, and Windy.

Script representation example with a scenario of a student taking an exam, including preparation, examination, and post-examination steps.

Characteristics of good knowledge representation include being explicit, complete, and efficient in displaying natural limitations.

Conclusion of the group's presentation on the material of knowledge representation.