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Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.

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Presentation on theme: "Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling."— Presentation transcript:

1 Modelling with expert systems

2 Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling with expert systems

3 Expert systems (1) Expert systems are computer programs designed to simulate expert reasoning in order to facilitate decision making for all sorts of problems. Artificial intelligence is a speciality in computer and cognitive sciences that focus on the development of programming techniques that enable machines to perform tasks that are regarded as intelligent when done by people. Expert system builders attempt to develop programs that simulate the human capability to reason and to learn. Expert systems may perform functions that simulate human thinking, such as decision making. Human intelligence is generalizable and transferable to new situations, but most forms of computer intelligence are not. An expert system queries the individual about the current status of a problem, searches its knowledge base for facts and rules, processes the information, arrives at decisions and reports the solution to the user. Terminology

4 Expert systems (2) Components of expert systems. The components include: The user: The computer must await input from a user with a need or problem. Current problem information: Data about the current situation is collected from the user into computer memory to help guide the expert system to a solution. User interface: The user interface facilitates the expert systems communication with the user. The communication process gathers current problem data from the user, explains the experts reasoning and presents the solution or advice from the problem being solved. The interface also provides explanations about the questions being asked and the decisions being made. Terminology

5 Expert systems (3) Knowledge base: The knowledge base is composed of facts about objects and rules about the relationships among those objects that represent knowledge structures used by a human expert to reach a decision. Sets of IF conditions are combined using conjunctions (condition 1 AND condition 2 must exist), disjunctions (condition 1 OR condition 2 must exist), and negations (condition 1 but NOT condition 2 must exist) for a decision to be reached. Expert editor: An editor enables the expert or the knowledge engineer to enter information into the knowledge base. Editors consist of text editors and parsers. The text editor allows the engineer to input facts and rules into the knowledge base in a prespecified format. More … Terminology

6 Expert systems (4) The parser checks the syntax of information that is input as well as the validity or logic of the information. Inference engine: The inference engine is the part of an expert system that functions intelligently while querying the knowledge base. The inference engine does its work after the user poses a specific problem and enters current problem information. The inference engine contains the logical programming to examine the information provided by the user, as well as the facts and rules specified within the knowledge base. The inference engine evaluates the current problem, situation and seeks rules that will provide advice about that situation. Solution or advice: The expert system presents a solution generated by the inference engine based on the permanent knowledge base and current problem information.

7 Modelling with expert systems Terminology The primary use of expert systems is to provide intelligent advice to novices who request it. Users query the knowledge base to get help in making decisions. Expert system advisors have also been developed to guide novices through the instructional development process. Learners gain more understanding form building expert systems. The construction of expert systems provides an intellectual environment that demands the refinement of domain knowledge, supports problem solving and monitors the acquisition of knowledge. Modelling domain knowledge requires identifying declarative knowledge (facts and concepts). Structural knowledge (knowledge of interrelationships among ideas) and procedural knowledge (how to apply declarative knowledge) that an expert possesses.

8 Steps when coaching construction of expert systems (1) Terminology Steps when coaching construction of expert systems Step 1: Learners make a plan. Learners need to answer the following questions: What kind of structure and information are needed? What are the learning goals? Step 2: Learners identify the purpose for building an expert system. The goal can be to master problem solving. Step 3: Learners specify problem solutions or decisions. Identify the solutions, decisions and outcomes of the expert system. Step 4: Learners isolate problem attributes, factors and variables. Gather and analyze information and then decide what other information is needed to solve the problem.

9 Terminology Step 5: Learners generate rules and examples. Rules represent the knowledge or expertise in an expert system. Rules consist of two elements: the premise (antecedent) and the conclusion (consequent). The premise begins with the word if and states the conditions that are compared with the situation or the desires of the user. Conditions are combined logically using the logical operators and and or. Conclusions are signalled with the word then. Step 6: Learners refine logic and efficiency of decision making. Step 7: Learners test the system. Steps when coaching construction of expert systems (2)

10 Advantages and limitations of modelling with expert systems Expert systems focus thinking on casual reasoning and problem solving. Expert systems engage learners in metacognitive reasoning. Expert systems emphazise inferential and implicational reasoning. The process of building expert systems requires novel thinking for many learners, so the work is difficult. Formal operational reasoning is required. Expert systems most benefited learners with higher abstract reasoning ability. Learners with lower abstract reasoning ability are not affected. Terminology

11 More … Terminology User interface Solution/adviceInference engine Knowledge base Expert editor Current problem information User Expert system

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