Thermostat problem

Thermostat problem - Project Title RULE-BASED EXPERT SYSTEM...

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Project Title RULE-BASED EXPERT SYSTEM - THERMOSTAT Artificial Intelligence Course Code: DT3010 Date of Submission: 16-10-10 Submitted by: Syed Mozammel Hossain 820201T030 Department of computer Science Dalarna University Borlange, Sweden
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Introduction The project titled “RULE-BASED EXPERT SYSTEM - THERMOSTAT” is based on expert system with rule based scheme. That means expert system is premeditated by some predefined rules. Here I develop the project in CLISP which is the most common and popular platform for developing rule based expert system. The aim of this project is to generate a result of the problem of thermostat setting based on rules. Expert/ Knowledge-Based Systems: The primary intention of expert system technology is to understand the integration of human expertise into computer processes. This integration not only helps to preserve the human expertise but also allows humans to be untied from performing the more routine manners that might be associated with interactions with a computer-based system. Given the number of textbooks, journal articles, and conference publications about expert/knowledge based systems and their application, it is not surprising that there exist a number of different definitions for an expert/knowledge-based system. In this piece of writing I use the following definition. An expert/knowledge-based system is a computer program that is designed to imitate the decision-making ability of a decision-maker(s) (i.e., expert(s)) in a particular narrow domain of expertise. A computer program is a piece of software, written by a “programmer” as a solution to some particular problem or client need. Because expert/knowledge-based systems are software products they inherit all of the problems associated with any piece of computer software. An expert/knowledge-based system uses a decision-maker(s) (i.e., expert(s)). Webster‟s dictionary defines an expert as “One with the special skill or mastery of a particular subject” The crucial point in the improvement of an expert/knowledge-based system is to obtain and represent the knowledge and experience of a person(s) who have been identified as possessing the special skill or mastery.
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Forward-chaining : Reasoning has two methods; Forward-chaining is one of them which use inference rules (in AI). Backward chaining is the opposite of forward chaining. Forward chaining is starts with the existing data. It uses inference rules to eliminate more data (from an end user for example) until a target is reached. An inference engine using forward chaining searches the inference rules until it finds one where the predecessor (If clause) is known to be true. When found it can finish off, or deduce, the resulting (Then clause), ensuing in the addition of new information to its data. Backward Chaining:
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Thermostat problem - Project Title RULE-BASED EXPERT SYSTEM...

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