scholarly journals AN ARTIFICIAL INTELLIGENCE AND KNOWLEDGE-BASED SYSTEM TO SUPPORT THE DECISION- MAKING PROCESS IN SALES

2019 ◽  
Vol 30 (2) ◽  
Author(s):  
Ismael Cristofer Baierle ◽  
Miguel Afonso Sellitto ◽  
Rejane Frozza ◽  
Jones Luís Schaefer ◽  
Anderson Felipe Habekost
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Making Process ◽  
Knowledge Based System ◽  
Knowledge Based

Knowledge Elicitation and the Development of a Knowledge-Based System for Production Scheduling

1995 ◽  
Vol 209 (2) ◽  
pp. 99-106
Author(s):  
G Price ◽  
F P E Dunne ◽  
K S Teoh ◽  
D G Walters
Keyword(s):  
Decision Making ◽  
Production Scheduling ◽  
Knowledge Elicitation ◽  
Planning System ◽  
Decision Making Process ◽  
Prototype System ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Computer Based ◽  
Press Shop

A prototype knowledge-based system for automated production scheduling has been developed for a press shop, which manufactures laminations for stator and rotor packs and cores for electric motor production. The system is PC (personal computer) based, user friendly, and may be interfaced to the existing computer-based MRP (material requirements planning) system, making the transfer of production data quick and easy. The knowledge-based system has been tested and validated over a range of production circumstances and the predicted production schedules show close agreement with those manually produced, over longer time-scales, by the human scheduler. The knowledge elicitation procedures developed are described and, in particular, the use of a large flow diagram to describe the decision-making process during scheduling is discussed. The visual representation of the structure of the decision-making process is of particular benefit in aiding clarity of communication between the knowledge engineer and the domain expert. The techniques for knowledge acquisition within the production scheduling environment have been formalized and their use is advocated for use in the development of scheduling systems. For the prototype system to be of practical benefit within the press shop, further development of the system is necessary to enable all the presses and lamination combinations to be scheduled. This is currently being carried out within the company.

Development of a knowledge-based system supporting IPM decision making in apples

1990 ◽  
Vol 4 (4) ◽  
pp. 315-331 ◽  
Author(s):  
B. Huber ◽  
J.P. Nyrop ◽  
W. Wolf ◽  
H. Reissig ◽  
A. Agnello ◽  
...  
Keyword(s):  
Decision Making ◽  
Knowledge Based System ◽  
Knowledge Based

An Advisory Conceptual Design Tool for Mechanical Transmission Systems

1994 ◽  
Author(s):  
C. P. Huang ◽  
F. W. Liou ◽  
J. J. Malyamakkil ◽  
W. F. Lu
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Conceptual Design ◽  
Design Process ◽  
Design Tool ◽  
Mechanical Transmission ◽  
Transmission Systems ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Packaging Machine

Abstract This paper presents an advisory conceptual design tool for mechanical transmission systems. Space consideration was taken into account during the design process. A prototype function tree was built in the form of knowledge-based system to transfer a designer’s idea into a set of mechanical components. An advisory expert system was also developed to help a designer in decision making. As an example, a packaging machine is designed using the developed system.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

scholarly journals Context Analysis in the Development of Diving Technologies

2019 ◽  
Vol 67 (2) ◽  
pp. 7-58
Author(s):  
Ryszard Kłos
Keyword(s):  
Decision Making ◽  
Continuous Improvement ◽  
Previous Article ◽  
Decision Making Process ◽  
Context Analysis ◽  
New Approach ◽  
Knowledge Based ◽  
Research Cycle ◽  
Level Of Risk

Abstract The previous article described a new approach methodology1 for work on the development of technology for the use of the SCR CRABE SCUBA2 type diving apparatus. However, after its publication numerous questions emerged regarding the genesis of the research undertaken, also from foreign partners using the same rebreather. The work on changing the technology of use was preceded by analyses, which were available only to people involved in the decision-making process. Demonstrating all the details of the decision-making process may be tedious, but failing to present them at all might raise justified doubts about the advisability of conducting a long-term research cycle. This article only presents preliminary analyses. The necessity to perform them resulted from the specific requirements for military technologies3 which, as broadly as possible, should be knowledge-based. The knowledge-based approach by its very nature allows continuous improvement of the adequacy of the predictions made, the estimation of the level of risk when diagnosing deviations from the repeatability or precision of the model, and the possibility of adapting the technology to the changing requirements of the user resulting from tactical considerations of its use.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Artificial Intelligence, Human Intelligence, and Orientism Management Framework

2021 ◽  
pp. 1-30
Author(s):  
Luisa Dall'Acqua
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Makers ◽  
Human Intelligence ◽  
Decision Making Process ◽  
Knowledge Process ◽  
Management Framework ◽  
Theoretical Contribution ◽  
Practical Guideline

The chapter intends to be a theoretical contribution for developers in the field of artificial intelligence. It also means a practical guideline for leaders, as decision-makers, to manage tasks and optimize performance. The proposed approach interprets the fluid nature of the decision-making process looking at knowledge and knowledge activities as dynamic, adaptive, and self-regulative, based not only on well-known explicit curricular goals but also on unpredictable interactions and relationships between players. The knowledge process is emerging in human and biological, social, and cultural environments.

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