Dialog-Based Learning (DBL) for Adaptive Interface Agents and Programming-by-Demonstrations Systems

摘要

Many users of workstation and PC tools often have to perform the same task againand again. For example, a secretary might have to send out a dozen e-mail messages until she finds a free meeting room. Or someone preparing business charts has to draw many special tables with shadowing bars around. Unfortunately, today's macro facilities of such tools do not support the end user enough in constructing the required automation functions. In this report we propose a mechanism, called dialog-based learning (DBL), that shall provide the user of software tools exactly with a mechanism to teach new functions or to give hints or additional information to a program on how to perform a task better. Two applications will be considered: The first one is our experimental system RAP, a room reservation apprentice that will eventually overtake a secretary's task to search for a free meeting or lecture room. RAP analyzes the outgoing and incoming e-mail and constructs a finite state machine that can repeat the task of asking all room administrators until a free room is found. The key of RAP's learning is to ask the user for unknown message types (e.g., request, positive answer, etc.) and key-phrases (e.g., 'need a room') and to collect them in a thesaurus. Our second application is a demonstrational graphics editor that allows the user to teach it new functions by giving a few examples. Artificial intelligence, Dialog-based learning, Programming by demonstration, Interface agents, Office software, Graphics editor, Software Secretary.