Case Studies Of Shell and knowledge Base System:
The use of expert system technology
Before starting any development effort, we want to know wether a knowledge-based system approach is justified and preferred over a conventional approach (if the current conventional state-of-the-art can handle this particular problem). The expert system literature offers several methods to asses the potential or the lack of potential of an application for a knowledge-based system approach.
KNOWLEDGE-BASED EXPERT SYSTEMS
Human expert knowledge is a combination of a theoretical understanding in a given domain and a collection of heuristic problem-solving rules that experience has shown to be effective. Computer-based expert systems (also known as knowledge-based systems) can be constructed by obtaining this knowledge from a human expert and transforming it into a form that a computer may use to solve similar problems. The ‘expert’ program does not know what it knows through the raw volume of facts in the computer’s memory, but by virtue of a reasoning-like process of applying a set of rules to the knowledge. It chooses among alternatives, not through brute-force calculation, but by using some of the same rules-of-thumb that human experts use. Thus, an expert system can be described as a computer program that simulates the judgement and behavior of experts in a particular field and uses their knowledge to provide problem analysis to users of the software. There are several forms of expert systems that have been classified according to the methodology used.
Rule-based systems use a set of rules to analyses
information about a specific class of problems and recommend one or more possible solutions;
case-based reasoning systems adapt solutions that were used to solve previous problems and use them to solve new problems
Case Study 1: Shell in a Manufacturing Company
Description:
A manufacturing company wants to improve its production process by implementing a shell-based system to assist in decision-making and problem-solving.
Problem:
The manufacturing company faces challenges in identifying the root causes of production issues and implementing effective solutions to improve productivity and quality.
Solution:
The company decides to develop a shell-based system that utilizes a knowledge base of production best practices, historical data, and expert knowledge. The shell will be used to diagnose production issues, suggest appropriate solutions, and provide guidance to the operators in real-time.
Implementation:
The company starts by gathering data from various sources, including machine sensors, production logs, and operator feedback. This data is used to populate the knowledge base, which consists of rules, heuristics, and problem-solving strategies. The shell is designed to analyze real-time production data, compare it with the knowledge base, and generate recommendations for the operators.
Results:
After implementing the shell-based system, the manufacturing company experiences several benefits. Real-time monitoring and analysis of production data help identify the root causes of issues more quickly, enabling prompt action and minimizing downtime. The shell provides step-by-step instructions to operators, ensuring consistent problem-solving approaches and reducing human errors. Overall, the company achieves significant improvements in productivity, quality, and efficiency.
Case Study 2: Knowledge-Based System in a Medical Diagnosis
Description:
A healthcare institution wants to leverage a knowledge-based system to assist doctors in diagnosing complex medical conditions.
Problem:
Doctors face challenges in diagnosing complex and rare medical conditions due to the vast amount of medical knowledge and the difficulty of keeping up with the latest research and advancements.
Solution:
The healthcare institution decides to develop a knowledge-based system that utilizes a vast database of medical knowledge, including symptoms, diseases, treatment protocols, and research papers. The system will be used to assist doctors in making accurate and timely diagnoses.
Implementation:
The knowledge-based system is developed by building a comprehensive knowledge base that incorporates medical textbooks, research articles, and expert opinions. The system uses an inference engine to analyze patient symptoms, medical history, and test results. It then matches this information with the knowledge base to generate a list of possible diagnoses, along with suggested tests and treatment plans.
Results:
The implementation of the knowledge-based system brings numerous benefits to the healthcare institution. Doctors can access a vast pool of medical knowledge and stay up-to-date with the latest research and treatment protocols. The system assists doctors in making accurate and timely diagnoses, leading to improved patient outcomes and reduced misdiagnosis rates. Additionally, the system helps in knowledge sharing among doctors and provides a tool for continuous learning and professional development.
