Step 6 is a goal-assertion the input, another algorithm might result. Thus one could resolution that functions similarly to the goal-goal resolution break a into a[1],..., a [length(a)/2] and a [length(a)/ above. The final synthesized program is: 2 1],..., a[length(a)] and find an algorithm that recursively calls f on both the first and second halves of its f(x) if x NIL then 0 else car(x) f(cdr(x)).

Our descriptive vocabulary may still In this article I propose to describe an area of artificial contain proper names as modifiers but the explanatory intelligence (Al) research that I and several colleagues vocabulary now involves the impersonal qualities of an have been enaged in for a number of years.

This computer program accepts expressions in natural language as on-line input. It searches each expression for syntactic and semantic patterns. When a pattern match is discovered an appropriate reply is typed out in natural language so that a continuing dialogue develops between person and program. The dialogue is restricted to the context of interpersonal relations such as occurs in a psychiatric interview. The program is an interpreter/supervisor written in SUBALGOL and runs on a 32K IBM 7090 connected via a direct-data device to a PDP-1 and a Philco console.

Paper presented at the IPN Symposium 7: Formalized Theories of Thinking and Learning and Their Implications for Science Instruction.

To test the program during its development, MyeTs and his students would select especially difficult cases for considemtion, often ones drawn fTOm published clinical pathological confeTences in medical journals. AfteT seveTal years of testing and rf:finement of the knowledge base, the study outlined in the following chapteT was peTformed. To document the strengths and weaknesses of the pTogmm, the gTOUP performed a systematic evaluation of the pTOgTam's capabilities.

The work is also of interest because of its relation to psychological studies (Chapter 12) and explanation methodology (Chapter 16).

We have studied difficulties arising in the operations of the "first generation" of AI programs in medicine and have undertaken the development of knowledge representation structures to support needed improvements. The description of a patient in existing programs such as INTERNIST-I (Pople et al., 1975), PIP (see Chapter 6), and MYCIN (Shortliffe, 1976) starts from a single list of findings about the patient. Using a data base of associations between diseases and findings (or rules establishing those connections), these programs form an interpretation of the patient's condition that is essentially a list of possible diseases, ranked by a calculated estimate of likelihood or degree of belief in each. Researchers (Patil, 1979; Pople, 1977; Smith, 1978) have recognized the need to use notions such as causal relationships, temporal patterns, and aggregate disease categories in the description of a program's diagnostic understanding, but the mechanisms provided to do this have been too weak. For example, although causality appears as a term in descriptions in PIP and INTERNIST-I, in both cases its use is limited to guiding the propagation of likelihood measures. These programs fail to capture the human notion that explanation should rest on a chain of cause-effect deduction.

Nor are they claiming that experts do not use general principles for ordering search and selecting alternatives (cf.

Recent research in clinical diagnosis (Barrows et al., 1978; Elstein et al., 1978; McGuire and Bashook, 1978) contributed to a consensus about the general form of the process of clinical diagnostic reasoning. Cues in patient data suggest hypotheses, which are, in turn, tested against subsequent data of the case. The basic hypothetico-deductive process is shared by experienced and inexperienced diagnosticians alike, as are numerous parametric characteristics of the process, such as the percentage of data items to first hypotheses, the average number of hypotheses maintained in active consideration, etc. These studies, however, have generally neglected the content of diagnostic reasoning, that is, the knowledge base of medical subject matter involved in the diagnostic process. Yet, despite prevalent findings of lack of differences in the form of diagnostic reasoning as a function of experience, the few differential findings from these research efforts implicate the importance of the knowledge base.

Turing's test (see evaluation) smart instruments, 345