Document
... Method is applied in computer games to extract the non-player characters’ behavior logic rules based on human knowledge and experience, make the NPCs active reasonable and more like real human beings, and contribute to enhance computer games interest and intelligence ability. For example, in mil ...
... Method is applied in computer games to extract the non-player characters’ behavior logic rules based on human knowledge and experience, make the NPCs active reasonable and more like real human beings, and contribute to enhance computer games interest and intelligence ability. For example, in mil ...
CLASSICAL LOGIC and FUZZY LOGIC
... CLASSICAL LOGIC In classical logic, a simple proposition P is a linguistic, or declarative, statement contained within a universe of elements, X, that can be identified as being a collection of elements in X that are strictly true or strictly false. The veracity (truth) of an element in the proposi ...
... CLASSICAL LOGIC In classical logic, a simple proposition P is a linguistic, or declarative, statement contained within a universe of elements, X, that can be identified as being a collection of elements in X that are strictly true or strictly false. The veracity (truth) of an element in the proposi ...
A Tutorial on Cognitive Network Process for Business Applications
... discusses the basic concepts and usages of AHP with its limitations. This talk presents the notion of Primitive Cognitive Network Process (P-CNP), which revises the AHP approach with practical changes. Some AHP applications revised by CNP are demonstrated with a step-by-step demonstration. Compariso ...
... discusses the basic concepts and usages of AHP with its limitations. This talk presents the notion of Primitive Cognitive Network Process (P-CNP), which revises the AHP approach with practical changes. Some AHP applications revised by CNP are demonstrated with a step-by-step demonstration. Compariso ...
Neuro-fuzzy systems
... The weighted inputs xi o wi, where o is a t-norm and tconorm, can be general fuzzy relations too, not just simple products as in standard neurons The transfer function g can be a non-linear such as a sigmoid ...
... The weighted inputs xi o wi, where o is a t-norm and tconorm, can be general fuzzy relations too, not just simple products as in standard neurons The transfer function g can be a non-linear such as a sigmoid ...
Special issue: Computational intelligence models for image
... the convergence rate and to enhance diversity of object in the system, while fuzzy entropy is used as the evaluation criterion. The proposed model has been shown to be more effective than other evolutionary-based methods in terms of applicability and computational efficiency. A multi-objective image ...
... the convergence rate and to enhance diversity of object in the system, while fuzzy entropy is used as the evaluation criterion. The proposed model has been shown to be more effective than other evolutionary-based methods in terms of applicability and computational efficiency. A multi-objective image ...
Fuzzy-probabilistic logic for common sense
... probabilistic reasoning within classical higher-order logic. This correspondence is not exact. Indeed, the relation between implications and conditionals is controversial and has resulted in variants of probabilistic logics. Our view is that classical implication should be replaced by probabilistic ...
... probabilistic reasoning within classical higher-order logic. This correspondence is not exact. Indeed, the relation between implications and conditionals is controversial and has resulted in variants of probabilistic logics. Our view is that classical implication should be replaced by probabilistic ...
Chap 11: Artificial Intelligence II: Operational Perspective
... Evaluation of Fuzzy Logic • Haack argues that there are very few true candidates for which Fuzzy Logic is useful. Most problems can be solved using principles drawn from probability. The computer programs are much too complicated and thus Fuzzy Logic serves no useful purpose. • Fox has rebutted thi ...
... Evaluation of Fuzzy Logic • Haack argues that there are very few true candidates for which Fuzzy Logic is useful. Most problems can be solved using principles drawn from probability. The computer programs are much too complicated and thus Fuzzy Logic serves no useful purpose. • Fox has rebutted thi ...
PPT
... Features of fuzzy logic • In fuzzy logic, exact reasoning is viewed as a limiting case of approximate reasoning • In fuzzy logic, everything is a matter of degree • In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables • Inf ...
... Features of fuzzy logic • In fuzzy logic, exact reasoning is viewed as a limiting case of approximate reasoning • In fuzzy logic, everything is a matter of degree • In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables • Inf ...
Fundamentals of Computational Intelligence
... The course introduces novel concepts in computational intelligence based techniques. It includes concepts on knowledge based reasoning, fuzzy inferencing systems, connectionist modeling based on artificial neural networks, and deep learning. The course material is self-contained but could be used as ...
... The course introduces novel concepts in computational intelligence based techniques. It includes concepts on knowledge based reasoning, fuzzy inferencing systems, connectionist modeling based on artificial neural networks, and deep learning. The course material is self-contained but could be used as ...
NEW APROACHES IN ARTIFICIAL INTELLIGENCE: A GENDERED
... perspective to this new approach of Soft Computing and in particular to one of its early areas: Fuzzy systems. The concept of Fuzzy Set (also Fuzzy Logic) was conceived by Lotfi Zadeh in 1965, and it was defined as a problem-solving and control system methodology which is empirically-based rather th ...
... perspective to this new approach of Soft Computing and in particular to one of its early areas: Fuzzy systems. The concept of Fuzzy Set (also Fuzzy Logic) was conceived by Lotfi Zadeh in 1965, and it was defined as a problem-solving and control system methodology which is empirically-based rather th ...
on fuzzy intuitionistic logic
... T h e s t a r t i n g point in Fuzzy Intuitionistic Logic is to fuzzify t r u t h . We accept formulae t h a t have different t r u t h values. This corresponds to t h e use of sentences in everyday life; they m a y be t r u e 'in different ways'. By accepting different t r u t h values, we also bre ...
... T h e s t a r t i n g point in Fuzzy Intuitionistic Logic is to fuzzify t r u t h . We accept formulae t h a t have different t r u t h values. This corresponds to t h e use of sentences in everyday life; they m a y be t r u e 'in different ways'. By accepting different t r u t h values, we also bre ...
Fuzzy logic
Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. By contrast, in Boolean logic, the truth values of variables may only be 0 or 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific functions.The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Fuzzy logic had however been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.