This is an excellent guide for formulating an expert system because it depicts the development of expert systems from the planning stages into testing and implementation. The work shows how systems learn. The process of learning progresses from formal experience, inference drawing and heuristics in order to combine knowledge/experience to form judgments. An expert system contains a domain which is the subject of automation. An expert or group of experts/opinion is synthesized. The knowledge engineer functions as an intermediary between the computer and the users. A semantic network is a group of computerized representations of objects connected by nodes and links representing relations between common objects. The inference engine matches patterns of words/phrases. There is a process of forward/backward chaining. For instance, the forward chain utilizes and optimizes the use of data provided by users in order to consult the system in real time. The work integrates both theory and practice in presenting the various steps necessary to get an artificial intelligence system up and running from scratch. The book is perfect for students in computer science, higher mathematics and software engineering.
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