SOAR: Universal Sub-Goaling

Early researchers emphasized general problem solving procedures, as embodied in the General Problem Solver (GPS). Then, in the early 1980s, the importance of domain-specific knowledge was discovered. For a while it looked like specific knowledge would always be the key to successful problem solving. But that, too, was too simple an approach. It became clear that real-life problem solving would require both general principles and lots of specific knowledge.

How did the emphasis change, in problem solving research during the 1970s and early 1980s?

An attempt to combine these approaches was called SOAR (for State, Operator, and Result). The SOAR project involved dozens of cognitive scientists at different universities but is most often identified with Allen Newell, one of the co-programmers of the General Problem Solver and a prominent cognitive scientist. SOAR had three distinctive attributes:

What were the three general ideas behind SOAR?

1. It accepted the General Problem Solver approach as a whole. Like the original GPS program, it represented problems as a "space." To solve a problem, the computer attempted to specify a series of steps that led from one state to another. This was done by selecting operations or steps that reduced the distance from the current state to the ultimate goal (the procedure called "hill-climbing.")

2. SOAR used a library of if/then rules, like an expert system. When it found a familiar term in the "if" end of an equation, it executed the "then" part.

3. Whenever the program hit a snag or failed to locate a relevant if/then rule in its library, it treated this as a new problem. The "snag" or impasse became a "sub-goal" on the way to the main goal. (Waldrop, 1988)

What is "universal sub-goaling" and what function does it serve in SOAR?

Newell regarded universal sub-goaling as SOAR's greatest innovation. If SOAR ran into a problem solving a sub-goal, it made that problem another sub-goal. This was SOAR's way of dealing with unexpected difficulties. SOAR does not "break" when it hits an impasse; it treats the impasse as a new problem to be solved.


A sub-goaling process, as used in SOAR

Humans act like this. We create sub-goals many layers deep. For example, suppose you need to write a term paper. That is the goal. In order to write the paper, you must come up with a topic. That is a subgoal. Now you must solve the problem of getting to the library. To do that, you must find your bicycle or car keys, so that becomes a new subgoal. But first, you must propel yourself out of your chair. That becomes an immediate subgoal. So you adjust your posture a bit and push up with both hands...and so forth. You would execute many thousands of subgoals before actually walking into the library, and that would only be the beginning of the project of completing a term paper.

What behavior by humans resembles the operation of SOAR?

Humans commonly intertwine millions of sub-goals. We coordinate them with amazing ease. Most of this coordination is automatic, built up over years of practice from babyhood onward. As an adult, setting out to do some goal such as going to the library, or writing a term paper, or attending college, we set up a complex problem-solving process with literally millions of sub-goals along the way. This is the ordinary stuff of human thinking, the comprehensive forward planning ability that distinguishes our species. In imitating this mental capability, SOAR was probably on the right track toward human-like problem solving.

The main criticism of SOAR was that it did not do very well in handling unexpected sub-goals. When it hit a new difficulty, humans had to help out. (Perhaps this should be called the "baby-sitter problem.") Lindsay (1991) concluded, "SOAR has not yet shown it can avoid the charge of being programmed for each new task."

What was the main criticism of SOAR?

Newell died in 1992, a month after receiving national recognition for the SOAR project. By now, the original SOAR is obsolete, but in the long run something like it (using universal sub-goaling) will have to be programmed into computers, if they are ever to imitate the flexibility of human problem solving. In the meantime, the difficulty of "making a computer do it" has given cognitive scientists a fresh appreciation for the complexity of ordinary human cognitive processes.


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