Intelligent-personalagent interface for operating systems

A self-teaching intelligent-personal-agent interface (IPAI) for the VAX/VMS operating system has been designed and tested. Its performance is based on (1) a priori knowledge about the operating system in use, and about the specific user environment, and (2) learning by observing a user interact with the computer system. The interface combines a syntax-based approach, a memory-based approach, and an approach based on intelligent personal agents. The implemented system was tested by users who completed evaluation forms. The best and worst properties of the implemented system are highlighted. Experiments show that the memory-based intelligent-agent approach enables designing very flexible and adaptable systems that learn from past events without burdening the user. Memorized events enable good performance even in situations that were not anticipated by the designers. The classical rule-based approach achieves similar performance for expected tasks, yet is less adaptable to changes in environment and demands more programming efforts. The intelligent-personal-agents approach has been shown to be a promising direction with the expected trust and competence problems. For mundane, easy tasks, the intelligent-personal-agents approach is very close to application maturity, thus enabling a step-up in man-machine communication.