The Relation between HIP Time and Alpha Spectrum Power of EEG in Cognitive Shift from the Knowledge-Based to Rule-Based

2000 ◽  
Vol 44 (1) ◽  
pp. 287-287
Author(s):  
Mitsuhiko Karashima ◽  
Murako Saito
Keyword(s):  
Rule Based ◽  
Knowledge Based ◽  
Alpha Spectrum ◽  
Cognitive Shift

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.

Software Interface Evaluation: Modeling of Human Error

1993 ◽  
Vol 37 (5) ◽  
pp. 453-455 ◽  
Author(s):  
S. J. Wright ◽  
S. J. Packebush ◽  
D. A. Mitta
Keyword(s):  
Human Error ◽  
Interface Element ◽  
Trial And Error ◽  
Rule Based ◽  
High Frequencies ◽  
Software Interface ◽  
Knowledge Based ◽  
Cognitive Levels ◽  
Graphics Software ◽  
And Task

The purpose of this study was to use a human error model to evaluate a commercially available Macintosh-based graphics application based upon the frequencies and types of mistakes occurring during users' performance of designated tasks. The occurrence of high frequencies of knowledge-based and rule-based mistakes during the learning of an interface element would indicate that the element requires evaluation and possible redesign. This study involved five participants, all of whom were students at Texas A&M University. The participants were experienced Macintosh users with no experience using Macintosh graphics software. The graphics environment of interest was MacDraw II® 1.0 Version 2 (Schutten, Goldsmith, Kaptanoglu, and Spiegel, 1988). Ten drawings created with the program were used to examine participants' cognitive levels and types of errors made throughout the process of familiarizing themselves with this program. The first drawing was created to exemplify simple figures created with the graphics tools in the program to illustrate shading. The second through tenth drawings incorporated these figures in several arrangements. All drawings incorporated eight tools (or tasks), and each tool was used only once in each drawing. The results indicated significant differences in frequencies of error types, frequencies of errors between tasks and frequencies of errors between trials. There were also interactions between trial and error, and task and error.

Parallelism in knowledge-based machines

1989 ◽  
Vol 4 (1) ◽  
pp. 53-71
Author(s):  
Apostolos N. Refenes
Keyword(s):  
Expert Systems ◽  
Programming Languages ◽  
Parallel Machine ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Rule Based ◽  
Boltzmann Machines ◽  
Knowledge Based ◽  
Parallel Tasks ◽  
Language Classes

AbstractThe application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.

An analysis of aircraft maintenance incidents using psychological and cognitive engineering knowledge

2016 ◽  
Vol 60 (1) ◽  
pp. 1676-1680
Author(s):  
Man-wa Ng ◽  
Simon Y. W. Li
Keyword(s):  
Data Entry ◽  
Cognitive Engineering ◽  
Aircraft Maintenance ◽  
Main Task ◽  
Rule Based ◽  
Knowledge Based ◽  
Data Entry Error ◽  
Starting Point ◽  
Analysis System ◽  
Error Types

The aim of the current analysis is to complement existing studies of aircraft maintenance incidents by providing finer and more detailed explanations for their causes in terms of task and error types. A total of 109 aircraft maintenance incidents were analyzed with respect to knowledge and concepts from psychology and cognitive engineering. The skill, rule and knowledge-based framework by Rasmussen (1983) was used to identify the main task types involved in the incidents. Error types such as post-completion error, prospective memory failure and data-entry error were used as part of the analysis. System usability and the occurrence of interruptions, distractions and multitasking were also adopted as important factors in the analysis. Results suggest that more than 60% of the incidents involved rule-based performance. Almost 50% of the rule-based incidents can be explained in terms of the errors types and factors identified. This analysis provides a starting point for practitioners to discuss aircraft maintenance incidents using theoretically grounded concepts.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

scholarly journals Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM

2013 ◽  
Vol 7 (1) ◽  
pp. 45
Author(s):  
Nur Hasanah ◽  
Retantyo Wardoyo
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inference Engine ◽  
Rule Based ◽  
Knowledge Based ◽  
Menu Planning ◽  
Model Complex ◽  
Uncertainty Method ◽  
Management Expert ◽  
Diabetes Melitus

AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product.            Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu  AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average.            The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu

The level of filling-in and when it is cognitive

1998 ◽  
Vol 21 (6) ◽  
pp. 758-758
Author(s):  
Richard L. Gregory
Keyword(s):  
Gap Filling ◽  
Illusory Contours ◽  
Rule Based ◽  
Knowledge Based ◽  
Target Article

This informative and conceptually stimulating target article is very useful. I merely query whether the term “illusory contours” is appropriate for gap filling; “illusory surfaces” seems better – and “fictional surfaces” better still. These seem to be rule based rather than knowledge based, suggesting indeed the importance of distinguishing rules (analogous to syntax in language) from knowledge (equivalent to semantics) for classifying perceptual phenomena.

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.

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