By the end of this unit you should:
Work in pairs. Without reading your notes or the course website, explain the different types of knowledge.
Read the following passage to answer these questions
Expert systems are a branch of artificial intelligence that uses specialized knowledge to solve problems in a specific domain. These systems are capable of making decisions similar to a human expert. The information in an expert system is stored in the form of a knowledge base, and it uses a set of rules to interpret this information.
The first type is rule-based systems. These systems use a set of if-then rules to solve problems. For example, a medical diagnosis system might have a rule like "if the patient has a fever and a cough, then they may have the flu". The system applies these rules to the information it has to make a decision.
The second type is frame-based systems. These systems use frames, which are data structures for representing a stereotyped situation. Each frame has a number of slots, or attributes, which can contain values. For instance, a frame representing a car might have slots for color, make, model, and year.
The third type is fuzzy systems. These systems are designed to handle uncertainty and ambiguity. Instead of using exact values, fuzzy systems use degrees of truth, which are represented as values between 0 and 1. For example, a weather prediction system might say there's a 0.7 chance of rain tomorrow, instead of making a definite prediction.
Work in pairs. Discuss your answers to the two questions.
Work in pairs. Find the answers to the following questions in the text in Activity 2. When you both agree on the answer (and its location in the text), move on to the next question.
An expert system is a branch of artificial intelligence that uses specialized knowledge to solve problems in a specific domain.
The knowledge base in an expert system stores the information that the system uses to make decisions.
A rule-based system is a type of expert system that uses a set of if-then rules to solve problems.
An example of a rule in a rule-based system could be judging a patient has flu based on the symptoms of a fever and a cough.
A frame-based system is a type of expert system that uses frames, or data structures, to represent stereotyped situations.
A frame in a frame-based system representing a car might have slots for colour, make (e.g. Subaru), model (e.g. Outback), and year.
A fuzzy system is a type of expert system designed to handle uncertainty and ambiguity.
Fuzzy systems represent degrees of truth as values between 0 and 1.
A weather prediction system might say there is a 0.7 chance of rain tomorrow, instead of making a definite prediction.
A fuzzy system might be best for a situation where there is a lot of uncertainty, as it is designed to handle such scenarios.
Check the answers by clicking on the question.
Read the criteria that are used to evaluate whether a system is an expert system.
A system may be categorized as an expert system if the following criteria are fulfilled:
Work in pairs. Decide whether the systems listed below are expert or non-expert systems.
Grammarly is a rule-based expert system. Grammarly has a set of rules related to English grammar, punctuation, spelling, and stylistic norms. When a student types a sentence, Grammarly compares the sentence to its rule set.
Netflix is a frame-based expert system. Netflix uses frame-based systems to categorize movies and TV shows into different frames or classes (for example, action movies, romantic comedies, documentaries, etc.). Each frame has a number of attributes like director, actors, length, viewer ratings, etc. When a student watches a movie, Netflix fills in the relevant frame's slots with the movie's attributes. By comparing this frame to other frames, Netflix can make recommendations based on similarities.
ChatGPT is not an expert system. ChatGPT is a general-purpose conversational AI that can generate responses across a wide range of topics, not just a single narrow domain. It does not provide expert advice. Its responses are based on patterns it has learned, and it can sometimes generate incorrect or nonsensical responses.
Google and other search engines might be confused for expert systems because they can retrieve specific information from a vast database in response to user queries. However, search engines primarily function by matching keywords and employing complex algorithms to rank results, rather than using structured, domain-specific knowledge to provide expert decisions.
Weather prediction apps are examples of fuzzy expert systems. Weather forecasting is an uncertain process. Instead of giving absolute predictions, these systems provide probabilities.
The Roomba is not an expert system. It is designed to carry out a specific set of actions based on its inputs (sensor data), but it doesn't solve complex problems in a specific knowledge domain like an expert system would.
Read the 8 most frequent ways to visualize expert systems.
Work in pairs or threes. Use TWO different ways to represent one of the systems listed below. Both ways must show exactly the same decision process. Submit your work on ELMS.
Can you:
If you do not, make sure that you do before your next class.
Running count: 38 of 38 concepts covered so far.