A Fuzzy Set Analysis of Skill, Rule and Knowledge-Based Workload

1988 ◽  
Vol 32 (2) ◽  
pp. 164-167 ◽  
Author(s):  
Neville Moray ◽  
Eugene Kruschelnicky ◽  
Paul Eisen ◽  
Laura Money ◽  
I. B. Turksen
Keyword(s):  
Task Difficulty ◽  
Linguistic Variables ◽  
Combination Rules ◽  
Rule Based ◽  
Fuzzy Measures ◽  
Knowledge Based ◽  
Fuzzy Set Analysis ◽  
Complete Set ◽  
Fuzzy Linguistic ◽  
Systematic Framework

Using fuzzy measures of task difficulty the effect of combining different aspects of a task on the overall task workload was explored. A complete set of combination rules is provided, of the general form “if the skill-based component is slightly difficult, the rule-based moderately difficult, and the knowledge-based slightly difficult, then the task as a whole is moderately difficult.” Fuzzy linguistic variables provide an adequate systematic framework for such combined judgments.

scholarly journals A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Neda Mohammadhasan ◽  
Susan Bastani
Keyword(s):  
Performance Evaluation ◽  
Expert System ◽  
Intellectual Capital ◽  
Fuzzy Rule ◽  
Qualitative Evaluation ◽  
Linguistic Variables ◽  
Rule Based ◽  
Proposed Model ◽  
Linguistic Approach ◽  
Fuzzy Linguistic

A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

INTERACTION BETWEEN AGENTS IN ROUTINE, FAMILIAR AND UNFAMILIAR SITUATION

1996 ◽  
Vol 05 (01) ◽  
pp. 1-25 ◽  
Author(s):  
B. CHAIB-DRAA
Keyword(s):  
Multiagent System ◽  
Urban Traffic ◽  
Rule Based ◽  
Complex Tasks ◽  
Knowledge Based ◽  
Complex Interactions ◽  
Low Levels

A framework for designing a Multiagent System (MAS) in which agents are capable of coordinating their activities in routine, familiar, and unfamiliar situations is proposed. This framework is based on the Skills, Rules and Knowledge (S-R-K) taxonomy of Rasmussen. Thus, the proposed framework should allow agents to prefer the lower skill-based and rule-based levels rather than the higher knowledge-based level because it is generally easier to obtain and maintain coordination between agents in routine and familiar situations than in unfamiliar situations. The framework should also support each of the three levels because complex tasks combined with complex interactions require all levels. To permit agents to rely on low levels, a suggestion is developed: agents are provided with social laws so as to guarantee coordination between agents and minimize the need for calling a central coordinator or for engaging in negotiation which requires intense communication. Finally, implementation and experiments demonstrated, on some scenarios of urban traffic, the applicability of major concepts developed in this article.

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