Different ways to support intelligent assistant systems by use of machine learning methods

Intelligent assistant systems provide an adequate organization of human-computer interaction for complex problem solving. These knowledge-based systems are characterized by a cooperative problem-solving procedure. User and system cooperate intensively to produce the aimed result. Machine learning methods can provide significant support for assistant systems. In this article, it is pointed out how assistant systems can be supported in various ways. For instance, machine learning methods can extend, revise, optimize, and adapt the knowledge base of an assistant system. In this way, they can contribute to the utility and maintainability of an intelligent assistant system. They can also increase the flexibility and effectiveness of human-computer interaction. The learning apprentice system COSIMA is presented which acquires knowledge about single problem-solving steps from observation of the user. Production rules for floorplanning, a sub-task of VLSI design, are acquired and refined cooperatively by different learning strategies.