Maintaining consistency of database during monitoring of an evolving process by a knowledge-based system

A method of managing and updating a database of facts for engineering systems that change their states over time is proposed. Plan selection and monitoring of dynamically evolving processes involves: (1) deciding whether or not action is warranted based on information about the current state of the system, (2) choosing an appropriate set of actions to change its state, and (3) inferring the effect of those actions on the system's state. The proposed methodology is based on the assumption that the monitored system follows a logical model. An intuitive definition of this logical model and a short justification of this assumption are presented. The knowledge-acquisition system (Super) that was developed to build a network of rules that represents this logic model and the properties of the knowledge-base structure are briefly presented. A strategy to maintain and update the database is defined. Data are correctly inserted, deleted, or changed by using the definition of consistency based on the network of rules. The case of rules that represent the changes of states of the system in response to external actions is developed. Two solutions are presented, depending on whether the history of the state changes is maintained or not. A looped inference engine is introduced to ensure goals analysis and plan selection according to the past and present data. The strategy is based on assigning a goal a rating depending on two factors: the ratio of the number of supporting facts to a function of the total number of facts in the database, and the relationships of the supporting facts to the goals. >

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