Expert Systems for Experts

This book contains important details on knowledge-basedsystems formulation and implementation. The author describes thetheory of forward chaining which involves matching rules againsta factbase to find successful matches, conflict resolution ofthe data and action after concluding a specific alternativefrom the various rule structures.The author describes the expert system life cycle consisting of:o feasibility analysis of the domain and task studiedo conceptual design of how the expert system carries out taskso knowledge acquisition gets the knowledge from human experts and case sourceso validationo technical transfer and maintenanceo choice of a manageable task or domain of possibilitiesKnowledge is stored in frames with slot names and values.In backward chaining, the system has a goal to prove and itworks backwards to prove it gaining the evidence with eachiteration. By focusing on conclusions, the system will identifywhat caused the pattern of symptoms. The system attempts tomatch conclusions in the database until it finds one. Inaddition, the system may prove the rule premise. If a matchingrule is found, the system will try to prove each premise in therule. Lastly, the AI system may try to backtrack within a premise. If a clause fails, go to the previous clause andtry to find new bindings for variables in the clause. In the event everything fails, find a new rule structure.This work is a treasure chest of information for anyone seekingto design a workable AI system utilizing sound programmingand logic principles.