Case Based pada Sistem Pakar (Penjelasan, Contoh, & Case Based Reasoning) | Artificial Intelligence

Summary

In this video, Vida Mastrika explains Case-Based Reasoning (CBR) as a key concept in artificial intelligence. She contrasts CBR with rule-based systems, describing how case-based systems rely on past experiences to solve new problems by retrieving similar cases and applying corresponding solutions. The video outlines the CBR process, which includes retrieval, reuse, revision, and retention of cases. Examples like selecting a smartphone based on user needs illustrate how cases are structured and used in AI decision-making systems. The video concludes with a brief mention of forward chaining, to be discussed in the next video.

Takeaways

• 😀 Rule-based and case-based reasoning are both used in knowledge bases for problem-solving, but they differ in approach: rule-based is deterministic while case-based relies on past experiences.
• 😀 Case-based reasoning (CBR) is based on past cases, which consist of a problem description and its solution, and can vary depending on context.
• 😀 A knowledge base using case-based reasoning stores multiple cases, not just one, to help solve new problems with similar features.
• 😀 The main process of case-based reasoning includes four steps: Retrieve, Reuse, Revise, and Retain.
• 😀 Retrieval involves finding a case similar to the current problem from the knowledge base, while Reuse involves applying the solution of that case.
• 😀 If the reused solution is not fully suitable, the system revises the solution to make it fit the current situation.
• 😀 After revising, the updated case is retained in the knowledge base as a learned case for future use.
• 😀 The process of case-based reasoning is iterative and dynamic, where cases are continually updated and refined to improve decision-making.
• 😀 In the mobile phone purchase example, cases are defined by features like user type, specifications, and budget, helping the system propose appropriate solutions.
• 😀 Case-based reasoning systems can adapt to new situations by comparing current problems with past cases, which are flexible and context-dependent.
• 😀 The learning aspect of case-based reasoning helps the system evolve over time by adding new cases or adjusting existing ones for better future predictions.

Q & A

What is the difference between rule-based systems and case-based systems in AI?

Rule-based systems rely on predefined rules (if-then statements) to make decisions, while case-based systems use past experiences or previous cases to solve current problems by comparing new situations with similar past ones.

What is the role of the knowledge base in case-based reasoning?

The knowledge base in case-based reasoning stores all previous cases, including their situations and solutions, which are used by the system to find and apply similar solutions to new problems.

What are the main components of a case in a case-based system?

A case in a case-based system consists of two main components: a description of the situation (the problem or scenario) and the solution or decision made to address that situation.

How does the system retrieve a relevant case in case-based reasoning?

In case-based reasoning, the system retrieves a relevant case by comparing the current situation with stored cases in the knowledge base and selecting the case that closely matches the current problem.

What happens during the 'reuse' step of case-based reasoning?

During the 'reuse' step, the system applies the solution from the retrieved case to the new problem, assuming it will work in a similar context.

Why is the 'revise' step necessary in case-based reasoning?

The 'revise' step is necessary because the solution from the retrieved case may not fully apply to the new situation. The solution is adjusted or refined to better suit the current problem.

What does the 'retain' step involve in case-based reasoning?

In the 'retain' step, the revised solution is stored back into the knowledge base, either as a new case or an updated version of an existing case, for future use and learning.

Can a case-based system operate with only a single case in its knowledge base?

No, a case-based system relies on a collection of cases in the knowledge base to perform effective problem-solving. The more cases it has, the better it can handle diverse situations.

How is the process of case-based reasoning iterative?

Case-based reasoning is iterative because after the solution is applied and revised, the system stores the updated case in the knowledge base and the cycle repeats when a new problem arises. The system continues to improve its knowledge over time.

In the example of buying a phone, how does the system make a recommendation based on the user's needs?

In the phone-buying example, the system compares the user's needs (e.g., budget, camera quality) with similar past cases in the knowledge base. It then recommends a phone based on the most relevant previous case, adjusting the solution if necessary to meet the current user's requirements.