Expert Systems as Cognitive Tools for Human Decision Making

1987 ◽  
pp. 269-288 ◽  
Author(s):  
Franz Schmalhofer
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Cognitive Tools ◽  
Human Decision

Instruction Modeling

2020 ◽  
pp. 78-106
Author(s):  
George A. Khachatryan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert Systems ◽  
Classroom Teachers ◽  
High Quality ◽  
Teaching Expertise ◽  
Intelligence System ◽  
The Core ◽  
Russian Mathematics ◽  
Human Decision

This chapter describes the core ideas behind instruction modeling. A promising way to improve mathematics instruction is to import successful approaches from other countries; however, it is exceptionally difficult to do this, since instructional traditions are cultural and the volume of teaching expertise that needs to be transferred is vast. Computers offer a possible way to ease the barriers. Expert systems (invented c. 1970) are a type of artificial intelligence system that uses rules to mimic human decision-making. Following the pattern suggested by expert systems, an instruction modeler studies high-quality offline instruction and then designs computer programs that aim to recreate this instruction. Many important activities cannot be automated, and therefore instruction modeling is necessarily blended learning: some instruction takes place online, while other activities are led by classroom teachers. To illustrate these ideas, this chapter describes several instruction modeling programs created by Reasoning Mind. It also discusses Russian mathematics education, explaining why it is a successful instructional tradition and a suitable choice for instruction modeling.

Strategies and biases in human decision-making and their implications for expert systems

1986 ◽  
Vol 5 (2) ◽  
pp. 119-140 ◽  
Author(s):  
VARGHESE S. JACOB ◽  
LARRY D. GAULTNEY ◽  
GAVRIEL SALVENDY
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Human Decision

Rxpert: A Prototype Expert System for Formulary Decision Making

1992 ◽  
Vol 26 (2) ◽  
pp. 244-250 ◽  
Author(s):  
Marianne L. Greer
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Drug Product ◽  
Computer Assisted ◽  
Health Related ◽  
Human Decision ◽  
Decision Making Processes ◽  
Complex Decision Making

OBJECTIVE: RXPERT, a prototype, computer-based, expert system that models the decision-making processes for an ambulatory (non-hospital) formulary, is described as an example of how expert systems may be used to support pharmacy decision making. Basic information about expert-system technology is provided through this example. BACKGROUND: Computer-assisted decision making is becoming an important and accepted aspect of complex, health-related decisions. Because expert-system support may become an integral component of future, complex, pharmacy decision making, it is important for pharmacists to become familiar with this technology and its possibilities for supporting pharmacy decisions. METHOD: Expert systems offer the potential advantages of making the human decision-making process explicit, more consistent, easily duplicated in many locations simultaneously, and easy to update and document. Although an expert system is seldom intended to replace human decision makers, it can provide valuable support for complex, multivariable decisions. Typical knowledge-acquisition and knowledge-engineering techniques, as well as the characteristics and structure of expert systems, are described, relative to the development of the RXPERT prototype. CONCLUSIONS: Although RXPERT is not yet in use, the process for using an expert system to support an individual committee member's personal assessment of a drug product is described. Decision-support expert systems are potentially useful to pharmacists in complex decision-making tasks.

Diabetes Mellitus - Evaluating the Diagnostic Probabilities

2011 ◽  
pp. 169-177
Author(s):  
Adi Armoni
Keyword(s):  
Diabetes Mellitus ◽  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Decision Maker ◽  
Knowledge Bases ◽  
Bayesian Theory ◽  
Logical Conclusion ◽  
Human Decision

The article examines the behavior of the human decision-maker. It surveys research in which about 90 physicians specializing in various fields and with different degrees of seniority participated. It tackles the question of whether it is possible to found the majority of the knowledge bases of the expert systems on the Bayesian theory. We will discuss the way of decision making conforming to the probabilities evaluated according to the Bayesian theory. The logical conclusion, therefore, is that the development of a knowledge base for an expert system founded on probabilities calculated in accordance with the Bayesian theory must be carried out in a controlled manner and depend on the parameters mentioned above.

scholarly journals Computer-Based Diagnostic Expert Systems in Rheumatology: Where Do We Stand in 2014?

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hannes Alder ◽  
Beat A. Michel ◽  
Christian Marx ◽  
Giorgio Tamborrini ◽  
Thomas Langenegger ◽  
...  
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Daily Practice ◽  
Cochrane Library ◽  
Validation Process ◽  
Excellent Performance ◽  
Future Developments ◽  
Human Decision ◽  
Computer Based ◽  
Symptoms And Signs

Background. The early detection of rheumatic diseases and the treatment to target have become of utmost importance to control the disease and improve its prognosis. However, establishing a diagnosis in early stages is challenging as many diseases initially present with similar symptoms and signs. Expert systems are computer programs designed to support the human decision making and have been developed in almost every field of medicine.Methods. This review focuses on the developments in the field of rheumatology to give a comprehensive insight. Medline, Embase, and Cochrane Library were searched.Results. Reports of 25 expert systems with different design and field of application were found. The performance of 19 of the identified expert systems was evaluated. The proportion of correctly diagnosed cases was between 43.1 and 99.9%. Sensitivity and specificity ranged from 62 to 100 and 88 to 98%, respectively.Conclusions. Promising diagnostic expert systems with moderate to excellent performance were identified. The validation process was in general underappreciated. None of the systems, however, seemed to have succeeded in daily practice. This review identifies optimal characteristics to increase the survival rate of expert systems and may serve as valuable information for future developments in the field.

Evaluating Human Decision-Making Theories With Monkey Data

2013 ◽  
Author(s):  
Scott D. Brown ◽  
Pete Cassey ◽  
Andrew Heathcote ◽  
Roger Ratcliff
Keyword(s):  
Decision Making ◽  
Human Decision

Evidence Supporting Theory of Shannon Capacity in Human Decision-Making

2013 ◽  
Author(s):  
Laurence T. Maloney ◽  
James Tee ◽  
Hang Zhang
Keyword(s):  
Decision Making ◽  
Shannon Capacity ◽  
Human Decision

History among the Junipers: Hosea 14:2–10 as Metahistoriography

2019 ◽  
Vol 63 (1) ◽  
pp. 105-116
Author(s):  
Mark W. Hamilton
Keyword(s):  
Decision Making ◽  
Moral Formation ◽  
Divine Agency ◽  
Human Decision

Abstract The dual endings of Hosea promoted reflection on Israel’s history as the movement from destruction to restoration based on Yhwh’s gracious decision for Israel. It thus clarifies the endings of the prior sections of the book (chs. 3 and 11) by locating Israel’s future in the realm of Yhwh’s activities. The final ending (14:10) balances the theme of divine agency in 14:2–9 with the recognition of human decision-making and moral formation as aspects of history as well. The endings of Hosea thus offer a good example of metahistoriography, a text that uses non-historiographic techniques to speak of the movements of history.

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