Inducing decision-making knowledge from data bases: an approach to automating knowledge acquisition

1990 ◽  
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Decision Making ◽  
Knowledge Acquisition ◽  
Data Bases

New Method of Knowledge Acquisition of Aid Decision-Making for Missile-Launching

2014 ◽  
Vol 1006-1007 ◽  
pp. 685-688
Author(s):  
Guo Bao Ding ◽  
Hao Xing ◽  
Lian Bing Wang ◽  
Dan Li
Keyword(s):  
Decision Making ◽  
Knowledge Acquisition ◽  
Power Supply System ◽  
Supply System ◽  
Pulse Power Supply ◽  
Causal Knowledge ◽  
Diagram Method ◽  
New Knowledge ◽  
Knowledge Acquisition Methods ◽  
Aid Decision

Acquiring causal knowledge of abnormity is essential to Missile-Launching reliably. There are lots of Knowledge Acquisition methods. But it is absence for usage and maintenance support process. So it is necessary to start the research on new knowledge acquisition technology of aid Decision-Making for Missile-Launching. Based on the Usage and Maintenance-Support Process, this thesis acquires knowledge with the ESD and CESD (Converse Event Sequence Diagram) method. First, this thesis gives the concept of CESD. Then, in order to adapt the CESD model of the complex systems more effectively, this paper expands the CESD framework and provides a software frame of computer aided ESD study. Finally, the operation of pulse power supply system is analyzed on the basis of the improved ESD and CESD. This sample shows the applicability of ESD and CESD methodology in knowledge acquisition technology of aid Decision-Making for Missile-Launching.

A Multi Autonomic Objects Flexible Workflow and its Knowledge Acquisition Supporting Engineering Design Process

2010 ◽  
Vol 97-101 ◽  
pp. 3341-3344
Author(s):  
Dong Bo Wang ◽  
Xiu Tian Yan ◽  
Ning Sheng Guo ◽  
Tao Li
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Literature Review ◽  
Knowledge Acquisition ◽  
Engineering Design ◽  
Design Process ◽  
Design Activity ◽  
Learning Method ◽  
Matlab Simulation ◽  
Engineering Design Process

In order to support the dynamic and creative Engineering Design Process (EDP) comprehensively, after a detailed literature review, a multi autonomic objects (AO) flexible workflow is applied into the supporting and management of EDP, its support for decision making, EDP evolution and design activity granularity is explained, finally and most importantly, a genetic algorithm-based AO knowledge learning method is proposed, the algorithm is demonstrated by a MATLAB simulation that it can satisfy the knowledge acquisition in EDP satisfactorily.

scholarly journals Knowledge Discovery in Data in Construction Projects

2016 ◽  
Vol 62 (2) ◽  
pp. 217-228 ◽  
Author(s):  
J. Szelka ◽  
Z. Wrona
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Construction Projects ◽  
Domain Expert ◽  
Knowledge Engineer ◽  
Computer Aided ◽  
Structured Problems ◽  
Decision Making Processes

Abstract Decision-making processes, including the ones related to ill-structured problems, are of considerable significance in the area of construction projects. Computer-aided inference under such conditions requires the employment of specific methods and tools (non-algorithmic ones), the best recognized and successfully used in practice represented by expert systems. The knowledge indispensable for such systems to perform inference is most frequently acquired directly from experts (through a dialogue: a domain expert - a knowledge engineer) and from various source documents. Little is known, however, about the possibility of automating knowledge acquisition in this area and as a result, in practice it is scarcely ever used. It has to be noted that in numerous areas of management more and more attention is paid to the issue of acquiring knowledge from available data. What is known and successfully employed in the practice of aiding the decision-making is the different methods and tools. The paper attempts to select methods for knowledge discovery in data and presents possible ways of representing the acquired knowledge as well as sample tools (including programming ones), allowing for the use of this knowledge in the area under consideration.

scholarly journals The Impact of Visualization Format and Navigational Options on Laypeople’s Perception and Preference of Surgery Information Videos: Randomized Controlled Trial and Online Survey (Preprint)

2018 ◽  
Author(s):  
Marie Eggeling ◽  
Martina Bientzle ◽  
Thomas Shiozawa ◽  
Ulrike Cress ◽  
Joachim Kimmerle
Keyword(s):  
Decision Making ◽  
Knowledge Acquisition ◽  
Online Survey ◽  
Medical Information ◽  
Cruciate Ligament ◽  
Survey Study ◽  
Factual Knowledge ◽  
Significant Group ◽  
Realistic Visualization ◽  
The Impact

BACKGROUND Patients need to be educated about possible treatment choices in order to make informed medical decisions. As most patients are medical laypeople, they find it difficult to understand complex medical information sufficiently to feel confident about a decision. Multimedia interventions such as videos are increasingly used to supplement personal consultations with medical professionals. Former research has shown that such interventions may have a positive effect on understanding, decision making, and emotional reactions. However, it is thus far unclear how different features of videos influence these outcomes. OBJECTIVE We aimed to examine the impact of visualization formats and basic navigational options in medical information videos about cruciate ligament surgery on recipients’ knowledge gain, emotions, attitude, and hypothetical decision-making ability. METHODS In a between-group randomized experiment (Study 1), 151 participants watched 1 of 4 videos (schematic vs realistic visualization; available vs unavailable navigational options). In a separate online survey (Study 2), 110 participants indicated their preference for a video design. All participants were medical laypeople without personal experience with a cruciate ligament rupture and were presented with a fictional decision situation. RESULTS In Study 1, participants who used navigational options (n=36) gained significantly more factual knowledge (P=.005) and procedural knowledge (P<.001) than participants who did not have or use navigational options (n=115). A realistic visualization induced more fear (P=.001) and disgust (P<.001) than a schematic video. Attitude toward the surgery (P=.02) and certainty regarding the decision for or against surgery (P<.001) were significantly more positive after watching the video than before watching the video. Participants who watched a schematic video rated the video significantly higher than that by participants who watched a realistic video (P<.001). There were no significant group differences with regard to hypothetical decision making and attitude toward the intervention. In addition, we did not identify any influence of the visualization format on knowledge acquisition. In Study 2, 58 of 110 participants (52.7%) indicated that they would prefer a schematic visualization, 26 (23.6%) preferred a realistic visualization, 17 (15.5%) wanted either visualization, and 9 (8.2%) did not want to watch a video at all. Of the participants who wanted to watch a video, 91 (90.1%) preferred to have navigational options, 3 (3.0%) preferred not to have navigational options, and 7 (6.9%) did not mind the options. CONCLUSION Our study indicates that the perception of medical information videos is influenced by their design. Schematic videos with navigational options are the most helpful among all videos to avoid negative emotions and support knowledge acquisition when informing patients about an intervention. The visualization format and navigational options are important features that should be considered when designing medical videos for patient education. CLINICALTRIAL Deutsches Register Klinischer Studien DRKS00016003; https://www.drks.de/drks_web/ navigate.do?navigationId= trial.HTML&TRIAL_ID=DRKS00016003 (Archived by WebCite at http://www.webcitation.org/746ASSAhN)

Knowledge acquisition and decision making based on Bayes risk minimization method

2018 ◽  
Vol 49 (2) ◽  
pp. 804-818 ◽  
Author(s):  
Mingliang Suo ◽  
Zhiping Zhang ◽  
Ying Chen ◽  
Ruoming An ◽  
Shunli Li
Keyword(s):  
Decision Making ◽  
Knowledge Acquisition ◽  
Bayes Risk ◽  
Risk Minimization ◽  
Minimization Method
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