Providing explanations for recommended actions is deemed one of the most important capabilities of expert systems (ES). There is little empirical evidence, however, that explanation facilities indeed influence user confidence in, and acceptance of, ES-based decisions and recommendations. This paper investigates the impact of ES explanations on changes in user beliefs toward ES-generated conclusions. Grounded on a theoretical model of argument, three alternative types of ES explanations A trace, justification, and strategy A were provided in a simulated diagnostic expert system performing auditing tasks. Twenty practicing auditors evaluated the outputs of the system in a laboratory setting. The results indicate that explanation facilities can make ES-generated advice more acceptable to users and that justification is the most effective type of explanation to bring about changes in user attitudes toward the system. These findings are expected to be generalizable to application domains that exhibit similar characteristics to those of auditing: domains in which decision making tends to be judgmental and yet highly consequential, and the correctness or validity of such decisions cannot be readily verified.

https://www.jstor.org/stable/pdfplus/249686.pdf

The impact of explanation facilities on user acceptance of expert systems advice

We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0269888998001040

Knowledge level modelling: concepts and terminology

Rule-based expert systems are surveyed. The most important considerations are representation and inference. Rule-based systems make strong assumptions about the representation of knowledge as conditional sentences and about the control of inference in one of three ways. The problem of reasoning with incomplete or inexact information is also discussed, as are several other issues regarding the design of expert systems.

http://i.stanford.edu/pub/cstr/reports/cs/tr/82/926/CS-TR-82-926.pdf

Principles of rule-based expert systems

Implementing imprecision in information systems

The authors report the results of a study on the use of information and decision making by practicing marketing managers. They examine the effect of managerial experience and decision programmabili...

Janice S. Aikins

Papers

The organization of expert systems, a tutorial☆

Retrospective on “The organization of expert systems, a tutorial”