Modeling Expert Knowledge and Reasoning in Context

2015 ◽  
pp. 18-31 ◽  
Author(s):  
Patrick Brézillon
Keyword(s):  
Expert Knowledge ◽  
Modeling Expert

Modeling expert knowledge

1990 ◽  
Vol 2 (3) ◽  
pp. 179-206 ◽  
Author(s):  
Mildred L.G. Shaw ◽  
J. Brian Woodward
Keyword(s):  
Expert Knowledge ◽  
Modeling Expert

An Empirical Comparison of Methods for Eliciting and Modeling Expert Knowledge

2002 ◽  
Vol 46 (3) ◽  
pp. 482-486 ◽  
Author(s):  
Robert R. Hoffman ◽  
John W. Coffey ◽  
Mary Jo Carnot ◽  
Joseph D. Novak
Keyword(s):  
Concept Mapping ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
Cognitive Task ◽  
Knowledge Elicitation ◽  
Leverage Points ◽  
Training Support ◽  
Decision Method ◽  
Critical Decision Method ◽  
Modeling Expert

The goal of this project in Human-Centered Computing was to apply a variety of methods of Cognitive Task Analysis (CTA) and Cognitive Field Research (CFR) to support a complete process going all the way from knowledge elicitation to leverage point identification and then to system prototyping, and also use this as an opportunity to empirically compare and evaluate the methods. The research relied upon the participation of expert, journeyman, and apprentice weather forecasters at the Naval Training Meteorology and Oceanography Facility at Pensacola Naval Air Station. Methods included Protocol Analysis, a number of types of structured interviews, workspace and work patterns analysis, the Critical Decision Method, the Knowledge Audit, Concept Mapping, and the Cognitive Modeling Procedure. The methods were compared in terms of (1) their yield of information that was useful in modeling expert knowledge, (2) their yield in terms of identification of leverage points (where the application of new technology might bring about positive change), and (3) their efficiency. Efficiency was gauged in terms of total effort (time to prepare to run a procedure, plus time to run the procedure, plus time to analyze the data) relative to the yield (number of leverage points identified, number of propositions suitable for use in a model of domain knowledge). CTA/CFR methods supported the identification of dozens of leverage points and also yielded behaviorally- validated models of the reasoning of expert forecasters. Knowledge modeling using Concept-Mapping resulted in over a thousand propositions covering domain knowledge. The Critical Decision Method yielded a number of richly-populated case studies with associated Decision Requirements Tables. Results speak to the relative efficiency of various methods of CTA/CFR, and also the strengths of each of the methods. In addition to extending our empirical base on the comparison of knowledge elicitation methods, a deliverable from the project was a knowledge model that illustrates human-centered computing in that it integrates training support and performance aiding.

A Methodology for Modeling Expert Knowledge for Development of Agent-Based Systems

2011 ◽  
pp. 210-233 ◽  
Author(s):  
Michael Bowman
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Intelligent Agents ◽  
Expert Knowledge ◽  
Rule Learning ◽  
Subject Matter Experts ◽  
Agent Based ◽  
Mixed Initiative ◽  
The Military ◽  
Modeling Expert

For intelligent agents to become truly useful in real-world applications, it is necessary to identify, document, and integrate into them the human knowledge used to solve real-world problems. This article describes a methodology for modeling expert problem-solving knowledge that supports ontology import and development, teaching-based agent development, and agent-based problem solving. It provides practical guidance to subject matter experts on expressing how they solve problems using the task reduction paradigm. It identifies the concepts and features to be represented in an ontology; identifies tasks to be represented in a knowledge base; guides rule learning/refinement; supports natural language generation; and is easy to use. The methodology is applicable to a wide variety of domains and has been successfully used in the military domain. This research is part of a larger effort to develop an advanced approach to expert knowledge acquisition based on apprenticeship multi-strategy learning in a mixed-initiative framework.

Using Expert Knowledge to Identify Visual Cues for Landmine Detection

2008 ◽  
Author(s):  
James J. Staszewski ◽  
Alan D. Davison ◽  
David J. Dippel ◽  
Julia A. Tischuk
Keyword(s):  
Visual Cues ◽  
Expert Knowledge ◽  
Landmine Detection

Influential individuals: Approach to modeling

2018 ◽  
pp. 114-131
Author(s):  
O. Yu. Bondarenko
Keyword(s):  
Social Interactions ◽  
Social Status ◽  
Experimental Approach ◽  
Expert Knowledge ◽  
Formal Models ◽  
Leader Influence ◽  
Learning Game ◽  
Game Theoretic ◽  
Asymmetric Interactions ◽  
Influential Individuals

his article explores theoretical and experimental approach to modeling social interactions. Communication and exchange of information with other people affect individual’s behavior in numerous areas. Generally, such influence is exerted by leaders, outstanding individuals who have a higher social status or expert knowledge. Social interactions are analyzed in the models of social learning, game theoretic models, conformity models, etc. However, there is a lack of formal models of asymmetric interactions. Such models could help elicit certain qualities characterizing higher social status and perception of status by other individuals, find the presence of leader influence and analyze its mechanism.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

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