A common representation for problem-solving and language-comprehension information

It is shown that problem solvers are primarily concerned with deep inferences in narrow domains, while language comprehenders are more concerned with shallow inference in broader areas, and a compromise position is suggested which will use both frames and predicate calculus.

Abstract

Many in Artificial Intelligence have noted the common concerns of problem-solving and language-comprehension research. Both must represent large bodies of real world knowledge, and both must use such knowledge to infer new facts from old. Despite this the two subdisciplines have, with minor exceptions, kept arm's length. So, for example, many in language comprehension have adopted some form of ‘frame’ representation, while problem-solving people have tended to use predicate calculus. In this paper I will first show that this is not merely idiosyncratic behavior, but rather stems from the different issues stressed by the two areas, problem solvers being primarily concerned with deep inferences in narrow domains, while language comprehenders being more concerned with shallow inference in broader areas. I will then suggest a compromise position which will use both frames and predicate calculus, and then show how this representation has features desired by both camps.