Apr 29, 2017 · Question 6 1 out of 1 points What does self-knowledge in an expert system (ES) mean? Selected Answer: Selected Answer : The ES can explain how it reached a conclusion. Answers: An ES understands itself in a very human sense. ... Course Hero is not sponsored or endorsed by any college or university. ...
Feb 27, 2017 · What does self-knowledge in an expert system (ES) mean? A) An ES understands itself in a very human sense. B) The ES "knows" that it exists. C) An ES understands the human decision maker. D) The ES can explain how it reached a conclusion. B
Answer: D Diff: 3 Page Ref: 477 26) What does self-knowledge in an expert system (ES) mean? A) An ES understands itself in a very human sense. B) An ES understands the human decision maker. C) The ES can explain how it reached a conclusion. D) The ES "knows" that it exists. Answer: D Diff: 2 Page Ref: 478 27) How does an expert system differ from conventional …
Mar 20, 2022 · What does self-knowledge in an expert system (ES) mean? A. An ES understands itself in a very human sense. B. The ES "knows" that it exists. C. An ES understands the human decision maker. D. The ES can explain how it reached a conclusion. 22. How does an expert system differ from conventional systems? A.
The knowledge worker (also called the user or decision-maker) gains access to the ES via the dialogue structure provided by user interface. The knowledge worker provides the ES with input and receives the system’s explanation of how it reached its conclusion. Output to the user usually comprises of taking text from the knowledge base and slotting them into a few predefined sentence formats. When allowing the user to use natural language in obtaining input, a misunderstanding could result because of the ambiguous characteristic of natural language. The use of natural language in the dialogue with the user relies on the meaning of the user’s utterances. The meaning of what exactly is meant lies in examining the surrounding context and is prone to be misunderstood. The naïve user is likely, by the system’s ability to read natural language, to be fooled into thinking that the ES absolutely understands what he (the user) means. The best way to solve this problem is for the ES to generate all possible interpretations of any sentence that could be ambiguous, feed it back to the user, and enquire of the user which interpretation he (the user) really meant. The easiest way to understand the user is not to allow conversation to be in any natural language, but rather via a predefined syntax, requesting preformatted specific input. Questions posed to user and explanations of conclusions that are reached can be given by taking the text provided by the author of the knowledge base and slotting it into a few predefined sentence formats.
The knowledge that is contained in the system determines the effectiveness of the ES (See Paragraph 2.3.4: p22). Knowledge engineering may contain the following steps (See Paragraph 6.2.2: p113): Definition of the problem
An expert shell is an ES without the domain-specific knowledge (Beynon-Davies 1991). Mallach (1994) refers to a shell as a pre-packaged inference engine. Construction time can be reduced by using an ES shell. Advantages of using a shell include (Beynon-Davies 1991):
Blackboards are not an alternative to frames and nets for storing knowledge. It is rather a complex method used in complex systems to record choices, guesses and decisions about what to do next. It is used in situations where more than one source of information is active. All knowledge sources have access to a shared database. It involves an architecture that allows the independent knowledge sources to communicate through the central device: the blackboard.
The mechanism that performs the search and reasoning in rule-based systems is called the inference engine. The inference engine is activated when the user initiates the consultation session. The inference engine finds the rules that match the given facts, selects which rule to execute and executes the rule by adding the deduced fact to the Working Memory (WM). The inference engine uses pattern matching to select the qualifying rules. The choice of which rule to fire is done by conflict resolution. The most commonly used conflict resolution strategy is the first found strategy. The first applicable rule is executed (Klein & Methlie 1995) or fired by applying rule deduction or using formal logic. A new fact is concluded and added to the working memory and new patterns found that match the new fact. This sequence of steps and the linking of facts and patterns and rules are known as chaining (Klein & Methlie 1995).
The following describes the interaction between the knowledge base and the inference engine: A production system typically consists of a rule base (knowledge base) and a rule interpreter (inference engine) that decides when to apply the rules to the data, goals and intermediate results in the working memory. The Working Memory (WM) holds the Object-Attribute Values (OAV) used to drive or fire the rules. The OAV triggers some rules in the WM by satisfying their conditions.
The power and effectiveness of the ES is equal to the quality of the knowledge it contains. The knowledge has to cope with high degrees of complexity and apply the best judgement. The acquiring of expert knowledge is crucial and involves the gathering of information about a domain usually from an expert. This information is incorporated in a computer program stored as a knowledge base. Obtaining knowledge from humans can be a difficult task. Hayes-Roth et al (1983), as referenced by Turban (1993) and Klein & Methlie (1995), views knowledge acquisition (See Paragraph 6.4: p122) or knowledge engineering as being composed of five stages: Identification or definition of the problem and the major characteristics of the problem
An expert system is AI software that uses knowledge stored in a knowledge base to solve problems that would usually require a human expert thus preserving a human expert’s knowledge in its knowledge base.
They can advise users as well as provide explanations to them about how they reached a particular conclusion or advice. Knowledge Engineering is the term used to define the process of building an Expert System and its practitioners are called Knowledge Engineers.
Example : There are many examples of an expert system. Some of them are given below –. MYCIN –. One of the earliest expert systems based on backward chaining. It can identify various bacteria that can cause severe infections and can also recommend drugs based on the person’s weight. DENDRAL –.
Architecture of an Expert System. Knowledge Base –. The knowledge base represents facts and rules. It consists of knowledge in a particular domain as well as rules to solve a problem, procedures and intrinsic data relevant to the domain. Inference Engine –.
One expert system may contain knowledge from more than one human experts thus making the solutions more efficient. It decreases the cost of consulting an expert for various domains such as medical diagnosis. They use a knowledge base and inference engine.
Expert systems can solve complex problems by deducing new facts through existing facts of knowledge, represented mostly as if-then rules rather than through conventional procedural code. Expert systems were among the first truly successful forms of artificial intelligence (AI) software.
The primary role of a knowledge engineer is to make sure that the computer possesses all the knowledge required to solve a problem. The knowledge engineer must choose one or more forms in which to represent the required knowledge as a symbolic pattern in the memory of the computer. Example : There are many examples of an expert system.