Knowledge-Based Systems: The Key to Power? (1969-1979)

The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions. Such approaches have been called weak methods, because they use weak information about the domain.

For many complex domains, it turns out that their performance is also weak. The only way around this is to use knowledge more suited to making larger reasoning steps and to solving typically occurring cases in narrow areas of expertise. One might say that to solve a hard problem, you almost have to know the answer already. The DENDRAL program (Buchanan et a/., 1969) was an early example of this approach. It was developed at Stanford, where Ed Feigenbaum (a former student of Herbert Simon), Bruce Buchanan (a philosopher turned computer scientist), and Joshua Lederberg (a Nobel laureate geneticist) teamed up to solve the problem of inferring molecular structure from the information provided by a mass spectrometer.

The input to the program consists of the elementary formula of the molecule (e.g., C^H^NCi), and the mass spectrum giving the masses of the various fragments of the molecule generated when it is bombarded by an electron beam. For example, the mass spectrum might contain a peak at in- 15 corresponding to the mass of a methyl (CHi) fragment. The naive version of the program generated all possible structures consistent with the formula, and then predicted what mass spectrum would be observed for each, comparing this with the actual spectrum. As one might expect, this rapidly became intractable for decent-sized molecules.

The DENDRAL researchers consulted analytical chemists and found that they worked by looking for well-known patterns of peaks in the spectrum that suggested common substructures in the molecule. For example, the following rule is used to recognize a ketone (C=O) subgroup: if there are two peaks at A"i and,r> such that

(a) x\ +.\i = M + 28 (M is the mass of the whole molecule);
(b) A"i — 28 is a high peak;
(c) A"2 — 28 is a high peak;
(d) At least one of AI and AT is high. then there is a ketone subgroup

Having recognized that the molecule contains a particular substructure, the number of possible candidates is enormously reduced. The DENDRAL team concluded that the new system was powerful because All the relevant theoretical knowledge to solve these problems has been mapped over from its general form in the [spectrum prediction component] ("first principles") to efficient special forms ("cookbook recipes"). (Feigenbaum el al, 1971).

The significance of DENDRAL was that it was arguably the first successful knowledge-intensive system: its expertise derived from large numbers of special-purpose rules. Later systems also incorporated the main theme of McCarthy's Advice Taker approach— the clean separation of the knowledge (in the form of rules) and the reasoning component. With this lesson in mind, Feigenbaum and others at Stanford began the Heuristic Programming Project (HPP), to investigate the extent to which the new methodology of expert systems could be applied to other areas of human expertise. The next major effort was in the area of medical diagnosis.

Feigenbaum, Buchanan, and Dr. Edward Shortliffe developed MYCIN to diagnose blood infections. With about 450 rules, MYCIN was able to perform as well as some experts, and considerably better than junior doctors. It also contained two major differences from DENDRAL. First, unlike the DENDRAL rules, no general theoretical model existed from which the MYCIN rules could be deduced. They had to be acquired from extensive interviewing of experts, who in turn acquired them from direct experience of cases. Second, the rules had to reflect the uncertainty associated with medical knowledge.

MYCIN incorporated a calculus of uncertainty called certainty factors (see Chapter 14), which seemed (at the time) to fit well with how doctors assessed the impact of evidence on the diagnosis. Other approaches to medical diagnosis were also followed. At Rutgers University, Saul Amarel's Computers in Biomedicine project began an ambitious attempt to diagnose diseases based on explicit knowledge of the causal mechanisms of the disease process.

Meanwhile, large groups at MIT and the New England Medical Center were pursuing an approach to diagnosis and treatment based on the theories of probability and utility. Their aim was to build systems that gave provably optimal medical recommendations. In medicine, the Stanford approach using rules provided by doctors proved more popular at first. But another probabilistic reasoning system, PROSPECTOR (Duda et al., 1979), generated enormous publicity by recommending exploratory drilling at a geological site that proved to contain a large molybdenum deposit. The importance of domain knowledge was also apparent in the area of understanding natural language. Although Winograd's SHRDLU system for understanding natural language had engendered a good deal of excitement, its dependence on syntactic analysis caused some of the same problems as occurred in the early machine translation work.

It was able to overcome ambiguity and understand pronoun references, but this was mainly because it was designed specifically for one area—the blocks world. Several researchers, including Eugene Charniak, a fellow graduate student of Winograd's at MIT, suggested that robust language understanding would require general knowledge about the world and a general method for using that knowledge. At Yale, the linguist-turned-Al-researcher Roger Schank emphasized this point by claiming, "There is no such thing as syntax," which upset a lot of linguists, but did serve to start a useful discussion. Schank and his students built a series of programs (Schank and Abelson, 1977; Schank and Riesbeck, 1981; Dyer, 1983) that all had the task of understanding natural language. The emphasis, however, was less on language per se and more on the problems of representing and reasoning with the knowledge required for language understanding.

The problems included representing stereotypical situations (Cullingford, 1981), describing human memory organization (Rieger, 1976; Kolodner, 1983), and understanding plans and goals (Wilensky, 1983). William Woods (1973) built the LUNAR system, which allowed geologists to ask questions in English about the rock samples brought back by the Apollo moon mission. LUNAR was the first natural language program that was used by people other than the system's author to get real work done. Since then, many natural language programs have been used as interfaces to databases. The widespread growth of applications to real-world problems caused a concomitant increase in the demands for workable knowledge representation schemes.

A large number of different representation languages were developed. Some were based on logic—for example, the Prolog language became popular in Europe, and the PLANNER family in the United States. Others, following Minsky's idea of frames (1975), adopted a rather more structured approach, collecting together facts about particular object and event types, and arranging the types into a large taxonomic hierarchy analogous to a biological taxonomy.

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