John.Laird%CMU-CS-ZOG@sri-unix.UUCP (12/05/83)
ise, the problem solver should be able to recover by using an appropriate subgoal. However, current AI problem solver are limited in their generality because they depend on sets of fixed methods and subgoals. In previous work, we investigated the weak methods and proposed that a problem solver does not explicitly select a method for goal, with the inherent risk of selecting an inappropriate method. Instead, the problem solver is organized so that the appropriate weak method emerges during problem solving from its knowledge of the task. We called this organization a universal weak method and we demonstrated it within an architecture, called SOAR. However, we were limited to subgoal-free weak methods. The purpose of this thesis is to a develop a problem solver where subgoals arise whenever the problem solver encounters a difficulty in performing the functions of problem solving. We call this capability universal subgoaling. In this talk, I will describe and demonstrate an implementation of universal subgoaling within SOAR2, a production system based on search in a problem space. Since SOAR2 includes both universal subgoaling and a universal weak method, it is not limited by a fixed set of subgoals or methods. We provide two demonstrations of this: (1) SOAR2 creates subgoals whenever difficulties arise during problem solving, (2) SOAR2 extends the set of weak methods that emerge from the structure of a task without explicit selection.