By the end of this unit you should:
Listen to this introduction to find out the name of your teacher and how to contact him on campus and via email.
Read the introduction below:
The official university course syllabus provides details of the grade percentages awarded to participation, quizzes and final assessment.
The course divides into two parts: (1) expert systems, and (2) prototype development. You will work individually, in pairs or teams to understand the theory and practical applications of different expert sysems. You will work in teams to develop a prototype. For students who can program in Python and prefer not to work in teams, you can form a team of one.
Active participation is defined (by me) as submitting assignments or completing assigned tasks via the learning management system ( ELMS ).
In general, each assignment or task is awarded either zero or 100%. Most assignments involve solving problems. This emoticon is used to remind you of these. Quizzes are conducted either online or live. The final assignment is the creation of a prototype expert system. For this assignment, you need to design, develop and evaluate an original tool. Your group will need to submit three items, namely the source code, a written report and a video evaluation.
Introduce yourself to your classmates. State your:
Once you have introduced yourselves, discuss what you know about expert systems.
Read the following.
The course divides into two parts:
In the knowledge acquisition part, we focus on the core concepts of time and tense. In the prototype developmet part we focus on visualization of language.
Expert systems
The first four units are dedicated to understanding the different types of expert systems and practising using, adapting and creating expert systems for increasing sophisticated tasks. The four units to be covered are:
The next five units focus on the practical application of expert systems. These units are designed to help your team create a high-fidelity prototype for an expert system that addresses your assigned or approved task. The five units to be covered are:
The final unit brings together the key concepts covered during the course, and itemizes all the technical terminology and concepts that you should understand by the end of the course.
Prototype development
In this part, different visualization tools are introduced. This is followed by a brief introduction to different natural language pipelines. The lion's share of this part will be spent on prototype development. This prototype needs to be evaluated and so methods of evaluation are also covered. The final unit aims to review the course, bringing together all the core concepts covered.
The courses comprises 14 sessions and 10 units. the first half of the course will focus on Units 1 to 5. The remainder of the course will focus on Units 6 to 10.
Read this general introduction to expert systems, which was generated by ChatGPT-4.
Expert systems are a category of artificial intelligence (AI) software. They use knowledge and procedures from experts in a certain field to solve complex problems. These systems are able to make decisions like human experts. The core of an expert system is the knowledge base. This is a structured database filled with expert knowledge. The knowledge is often represented as rules or facts. Another key part is the inference engine. This is a program that applies logic to the knowledge from the knowledge base. It forms conclusions, solves problems, or gives advice.
Forward chaining and backward chaining are methods used in expert systems, especially in rule-based systems, to infer conclusions from given data. Forward chaining starts with the known information and uses inference rules to extract more data until a desired goal is achieved. Backward chaining works in the opposite direction. It starts with a goal, and then looks for facts or rules that support this goal.
Expert systems can explain their reasoning. Expert systems have a feature called explanation facility. This makes them transparent and trustworthy. They are used in many fields. Medicine, finance, and engineering are some examples. They help make complex decisions, diagnose diseases, predict trends, and more. Expert systems cannot replace human experts completely. They cannot learn from experiences like humans. Yet, they are a valuable tool when human expertise is scarce or decisions must be made quickly.
Work in pairs. Without looking at the paragraph above, describe what an expert system is. Describe the main components of an expert system, and state the two abilities that a system does not possess.
Work in pairs. Discuss which of these expert systems you have used. How successful were the systems?
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Work in groups. Discuss possible expert systems that could be developed to address the following problems.
Can you:
If you do not, make sure that you do before your next class.
Running count: 8 of 30 concepts covered so far.