Abstract: A learning apparatus and method of an intelligent system according to the present invention relates to learning of an intelligent system that automatically controls a system on the basis of information about events generated in an external environment or services input by a user. In the learning, it is determined whether rules extracted from event data or service data are the same as rules already stored. Then, if it is determined that the rules are new rules, queries about the confidence of new rules are generated and provided to a user. The rules are updated on the basis of user's answers. Accordingly, it is possible to accurately grasp users' patterns by using very small amount of observation data as compared to an existing method, and to more easily sense variable patterns. Therefore, it is possible to obtain a more intelligent learning in the intelligent system.

Abstract: Disclosed is an apparatus and method of constructing a user behavior pattern based on an event log that is a record of events generated in a context-aware system environment. An apparatus and method of constructing a user behavior pattern based on an event log according to an embodiment of the invention intelligently and actively provides the most applicable service in consideration of a specific user and a location thereof to a user, on the basis of a constructed user behavior pattern.

Abstract: A learning apparatus and method of an intelligent system according to the present invention relates to learning of an intelligent system that automatically controls a system on the basis of information about events generated in an external environment or services input by a user. In the learning, it is determined whether rules extracted from event data or service data are the same as rules already stored. Then, if it is determined that the rules are new rules, queries about the confidence of new rules are generated and provided to a user. The rules are updated on the basis of user's answers. Accordingly, it is possible to accurately grasp users' patterns by using very small amount of observation data as compared to an existing method, and to more easily sense variable patterns. Therefore, it is possible to obtain a more intelligent learning in the intelligent system.