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Abstract : |
The kinds of intelligent computer assistants that we would like to be able to construct are very much like intelligent organisms in their own right. Imagine for a moment an intelligent organism trying to get along in the world (find enough food, stay out of trouble, satisfy basic needs, etc.). The most valuable service played by an internal knowledge base for such an organism is to repeatedly answer questions like "what's going on out there?", "can it harm me?", "how can I avoid/placate it?", "Is it good to eat?", "Is there any special thing I should do about it?", etc. To support this kind of activity, a substantial part of the knowledge base must be organized as a recognition device for classifying and identifying situations in the world. The major purpose of this situation recognition is to locate internal procedures which are applicable (appropriate, permitted, mandatory, etc.) to the current situation. In constructing an intelligent computer assistant, the roles of knowledge are very similar. The basic goals of food getting and danger avoidance are replaced by goals of doing what the user wants and avoiding things that the machine has been instructed to avoid. However, the fundamental problem of analyzing a situation (one established either linguistically or physically or by some combination of the two) in order to determine whether it is one for which there are procedures to be executed, or one which was to be avoided (or one which might lead to one that is to be avoided), etc. is basically the same. For example, one might want to instruct such a system to remind the user in advance of any upcoming scheduled meetings, to inform him if he tries to assign a resource that has already been committed, to always print out messages in reverse chronological order (when requested), to assume that "the first " refers to the first day of the upcoming month in a future scheduling context and the first day of the current month in a past context, etc., |
