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.

A major result of this research has been the development of a computer program that is intended to serve as a consultant in a number of medical problem areas. Here the considerations that underlie the program are discussed. The basic functions of the program are outlined in a nontechnical way, and an example of the use of the program is given. Then the results of the use of the program for several different medical problems are reviewed. Finally, an attempt is made to ascertain the potential of programs such as this in the delivery of appropriate medical care. Detailed reports on various aspects of this research are available in the literature (Gorry, 1967; 1968; Gorry and Barnett, 1968a; 1968b), and so the emphasis here will be on providing a general overview of the work and results obtained to date.

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

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.

Steps toward a theoretic foundation for complex knowledge-based CAI.

Here we consider the logical bases for rules: what kinds of arguments justify the rules, and what is their relation to a mechanistic model of the domain? We use the terms "explain" and "justify" synonymously, although the sense of "making clear what is not understood" (explain) is intended more than "vindicating, showing to be right or lawful" (justify).

This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a method for sound and complete causal model discovery in the presence of latent confounders and/or selection bias, that has worst case polynomial complexity of order $N^{2(k+1)}$ in the number of independence tests, for sparse graphs over $N$ nodes, bounded by node degree $k$. The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.

This paper introduces a novel encoding of Model Based Diagnosis (MBD) to Boolean Satisfaction (SAT) focusing on minimal cardinality diagnosis. The encoding is based on a combination of sophisticated MBD preprocessing algorithms and the application of a SAT compiler which optimizes the encoding to provide more succinct CNF representations than obtained with previous works. Experimental evidence indicates that our approach is superior to all published algorithms for minimal cardinality MBD. In particular, we can determine, for the first time, minimal cardinality diagnoses for the entire standard ISCAS-85 and 74XXX benchmarks. Our results open the way to improve the state-of-the-art on a range of similar MBD problems.

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of Structural Causal Models, especially for cyclic models.