scholarly journals Tingkat Kesulitan Dinamis Menggunakan Logika Fuzzy pada Game Musik Tradisional Jawa Tengah

2021 ◽  
Vol 5 (2) ◽  
pp. 56-64
Author(s):  
Kadhana Reya Wisinggya ◽  
Hanny Haryanto ◽  
T. Sutojo ◽  
Edy Mulyanto ◽  
Erlin Dolphina
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Digital Games ◽  
Way Of Thinking ◽  
Traditional Songs ◽  
The Given

The culture in Indonesia is very diverse, one of which is traditional songs. However, knowledge of traditional songs is still small. Digital Games can spread knowledge about traditional songs, one of which is Central Javanese traditional songs. However, the Game that is made still has static difficulties, so the Game cannot follow the player's ability, resulting in the player feeling bored and not wanting to continue the Game. To generate dynamic difficulties, methods in artificial intelligence can be applied to Games, one of which is Fuzzy. So in this study proposed the application of dynamic difficulties using Fuzzy Logic in music Games / Rhythm Games. Fuzzy Logic is built based on mathematical values and represents uncertainty, where this logic imitates the human way of thinking. Fuzzy Logic can convert crisp input values into fuzzy sets by performing fuzzification. After the input value is converted, the input will be entered into the set of rules provided. Each rule produces a different output. After the process is complete, the output value will be converted back to the crisp output value. Based on the research conducted, it is found that Fuzzy Logic can be applied to music Games where the Game can follow the player's ability based on the given rules.

scholarly journals Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5987
Author(s):  
Pier Luigi Gentili
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Quantum Mechanics ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Human Interaction ◽  
Cognitive Domain ◽  
Bayesian Probability ◽  
Total Probability ◽  
Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes’ formula are conceivable as fuzzy sets and Bayes’ inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

scholarly journals Zadeh's Centenary

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Ioan DZITAC
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Soft Computing ◽  
Special Issue ◽  
Introductory Paper ◽  
And Control

This is the introductory paper in a special issue on fuzzy logic dedicated to the centenary of the birth of Lotfi A. Zadeh published by International Journal of Computers Communications & Control (IJCCC). In 1965, Lotfi A. Zadeh published in the journal „Information and Control” the article titled „Fuzzy sets”, which today reaches over 117 thousand citations. The total sum of citations for all his papers is above 253 thousand. Based on the notion of fuzzy sets, fuzzy logic and the concept of soft computing emerged, bringing extremely important implications to the field of Artificial Intelligence (AI). In 2017, I published, whith F.G. Filip and M.J. Manolescu, a 42-page long paper in the IJCCC about the life and masterwork of Lotfi A. Zadeh, from which I will use some information in this material [15].

scholarly journals Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

2017 ◽  
Vol 12 (6) ◽  
pp. 748 ◽  
Author(s):  
Ioan Dzitac ◽  
Florin Gheorghe Filip ◽  
Misu-Jan Manolescu
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Soft Computing ◽  
50Th Anniversary ◽  
Fuzzy Sets Theory ◽  
Early Influence ◽  
Fuzzy Logic Theory ◽  
General Aspects ◽  
Sets Theory

In 1965 Lotfi A. Zadeh published "Fuzzy Sets", his pioneering and controversialpaper, that now reaches almost 100,000 citations. All Zadeh’s papers were citedover 185,000 times. Starting from the ideas presented in that paper, Zadeh foundedlater the Fuzzy Logic theory, that proved to have useful applications, from consumerto industrial intelligent products. We are presenting general aspects of Zadeh’s contributionsto the development of Soft Computing(SC) and Artificial Intelligence(AI),and also his important and early influence in the world and in Romania. Severalearly contributions in fuzzy sets theory were published by Romanian scientists, suchas: Grigore C. Moisil (1968), Constantin V. Negoita & Dan A. Ralescu (1974), DanButnariu (1978). In this review we refer the papers published in "From Natural Languageto Soft Computing: New Paradigms in Artificial Intelligence" (2008, Eds.: L.A.Zadeh, D. Tufis, F.G. Filip, I. Dzitac), and also from the two special issues (SI) of theInternational Journal of Computers Communications & Control (IJCCC, founded in2006 by I. Dzitac, F.G. Filip & M.J. Manolescu; L.A. Zadeh joined in 2008 to editorialboard). In these two SI, dedicated to the 90th birthday of Lotfi A. Zadeh (2011), andto the 50th anniversary of "Fuzzy Sets" (2015), were published some papers authoredby scientists from Algeria, Belgium, Canada, Chile, China, Hungary, Greece, Germany,Japan, Lithuania, Mexico, Pakistan, Romania, Saudi Arabia, Serbia, Spain,Taiwan, UK and USA.

scholarly journals Model of a Company Competitiveness Control by Means of Artificial Intelligence Tools

2021 ◽  
Vol 9 (2) ◽  
pp. 60-65
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Research Direction ◽  
Pricing Policy ◽  
Building Projects ◽  
A Company ◽  
Artificial Intelligence Systems

This article is devoted to the problem of introduction of artificial intelligence systems into the activity of building participants and other building projects stakeholders. An example of determination of a contractor inner potential by means of fuzzy sets is presented. It is recommended to contracting companies to accumulate information about the components of the competitiveness potential, specifically about: their activity diversification (concentration), resource efficiency (salesvolumetocompanyassetsquotient), pricing policy flexibility (level of discounts at tenders), business reputation level –business assurance level of a company (percentage of works executed and paid under the won tenders in the total value of the won tenders). A model of determination of the influence of the indicated parameters onto the portion of the won tenders in the total value of the submitted tenders, which is based on fuzzy logic, is offered. Creation of an information system, which is presented in the form of a database, which accumulates information during the whole lifecycle of a company, is the following research direction.

Dyadic Obligations over Complex Actions as Deontic Constraints in the Situation Calculus

2020 ◽  
Author(s):  
Jens Claßen ◽  
James Delgrande
Keyword(s):  
Artificial Intelligence ◽  
Deontic Logic ◽  
Action Theory ◽  
Prima Facie ◽  
Situation Calculus ◽  
Artificial Agents ◽  
Planning Techniques ◽  
Course Of Action ◽  
Reasoning About Action ◽  
The Given

With the advent of artificial agents in everyday life, it is important that these agents are guided by social norms and moral guidelines. Notions of obligation, permission, and the like have traditionally been studied in the field of Deontic Logic, where deontic assertions generally refer to what an agent should or should not do; that is they refer to actions. In Artificial Intelligence, the Situation Calculus is (arguably) the best known and most studied formalism for reasoning about action and change. In this paper, we integrate these two areas by incorporating deontic notions into Situation Calculus theories. We do this by considering deontic assertions as constraints, expressed as a set of conditionals, which apply to complex actions expressed as GOLOG programs. These constraints induce a ranking of "ideality" over possible future situations. This ranking in turn is used to guide an agent in its planning deliberation, towards a course of action that adheres best to the deontic constraints. We present a formalization that includes a wide class of (dyadic) deontic assertions, lets us distinguish prima facie from all-things-considered obligations, and particularly addresses contrary-to-duty scenarios. We furthermore present results on compiling the deontic constraints directly into the Situation Calculus action theory, so as to obtain an agent that respects the given norms, but works solely based on the standard reasoning and planning techniques.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

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