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# The Common Sense Informatic Situation

Contention: The key to reaching human-level AI is making systems that operate successfully in the common sense informatic situation.

In general a thinking human is in what we call the common sense informatic situation first discussed in [McCarthy, 1989]. It is more general than any bounded informatic situation. The known facts are incomplete, and there is no a priori limitation on what facts are relevant. It may not even be decided in advance what phenomena are to be taken into account. The consequences of actions cannot be fully determined. The common sense informatic situation necessitates the use of approximate concepts that cannot be fully defined and the use of approximate theories involving them. It also requires nonmonotonic reasoning in reaching conclusions.

The common sense informatic situation also includes some knowledge about the system's mental state.

A nice example of the common sense informatic situation is illustrated by an article in the American Journal of Physics some years ago. It discussed grading answers to a physics problem. The exam problem is to find the height of a building using a barometer. The intended solution is to measure the air pressure at the top and bottom of the building and multiply the difference by the ratio of the density of mercury to the density of air.

However, other answers may be offered. (1) drop the barometer from the top of the building and measure the time before it hits the ground. (2) Measure the height and length of the shadow of the barometer and measure the length of the shadow of the building. (3) Rappel down the building using the barometer as a measuring rod. (4) Lower the barometer on a string till it reaches the ground and measure the string. (5) Offer the barometer to the janitor of the building in exchange for information about the height. (6) Ignore the barometer, count the stories of the building and multiply by ten feet.

Clearly it is not possible to bound in advance the common sense knowledge of the world that may be relevant to grading the problem. Grading some of the solutions requires knowledge of the formalisms of physics and the physical facts about the earth, e.g. the law of falling bodies or the variation of air pressure with altitude. However, in every case, the physics knowledge is embedded in common sense knowledge. Thus before one can use Galileo's law of falling bodies , one needs common sense information about buildings, their shapes and their roofs.

Bounded informatic situations are obtained by nonmonotonically inferring that only the phenomena that somehow appear to be relevant are relevant. In the barometer example, the student was expected to infer that the barometer was only to be used in the conventional way for measuring air pressure. For example, a reasoning system might do this by applying circumscription to a predicate relevant in a formalism containing also metalinguistic information, e.g. that this was a problem assigned in a physics course. Formalizing relevance in a useful way promises to be difficult.

Common sense facts and common sense reasoning are necessarily imprecise. The imprecision necessitated by the common sense informatic situation applies to computer programs as well as to people.

Some kinds of imprecision can be represented numerically and have been explored with the aid of Bayesian networks, fuzzy logic and similar formalisms. This is in addition to the study of approximation in numerical analysis and the physical sciences.

Next: The Use of Mathematical Up: FROM HERE TO Previous: What is Human-Level AI?

John McCarthy
Sun Apr 19 15:21:34 PDT 1998