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Possibility theory : There are four cases that can be interpreted as follows: N ( U ) = 1 means that U is necessary. U is certainly true. It implies that Π ( U ) = 1 . Π ( U ) = 0 means that U is impossible. U is certainly false. It implies that N ( U ) = 0 . Π ( U ) = 1 means that U is possible. I would not ... |
Possibility theory : There is an extensive formal correspondence between probability and possibility theories, where the addition operator corresponds to the maximum operator. A possibility measure can be seen as a consonant plausibility measure in the Dempster–Shafer theory of evidence. The operators of possibility th... |
Possibility theory : We call generalized possibility every function satisfying Axiom 1 and Axiom 3. We call generalized necessity the dual of a generalized possibility. The generalized necessities are related to a very simple and interesting fuzzy logic called necessity logic. In the deduction apparatus of necessity lo... |
Possibility theory : Fuzzy measure theory Logical possibility Modal logic Probabilistic logic Random-fuzzy variable Transferable belief model Upper and lower probabilities |
Predicate (logic) : In logic, a predicate is a symbol that represents a property or a relation. For instance, in the first-order formula P ( a ) , the symbol P is a predicate that applies to the individual constant a . Similarly, in the formula R ( a , b ) , the symbol R is a predicate that applies to the individu... |
Predicate (logic) : A predicate is a statement or mathematical assertion that contains variables, sometimes referred to as predicate variables, and may be true or false depending on those variables’ value or values. In propositional logic, atomic formulas are sometimes regarded as zero-place predicates. In a sense, the... |
Predicate (logic) : Classifying topos Free variables and bound variables Multigrade predicate Opaque predicate Predicate functor logic Predicate variable Truthbearer Truth value Well-formed formula |
Predicate (logic) : Introduction to predicates |
Probabilistic database : Most real databases contain data whose correctness is uncertain. In order to work with such data, there is a need to quantify the integrity of the data. This is achieved by using probabilistic databases. A probabilistic database is an uncertain database in which the possible worlds have associa... |
Probabilistic database : In a probabilistic database, each tuple is associated with a probability between 0 and 1, with 0 representing that the data is certainly incorrect, and 1 representing that it is certainly correct. |
Probabilistic database : The first published use of the term "probabilistic database" was probably in the 1987 VLDB conference paper "The theory of probabilistic databases", by Cavallo and Pittarelli. The title (of the 11 page paper) was intended as a bit of a joke, since David Maier's 600 page monograph, The Theory of... |
Probabilistic database : The MayBMS project at Cornell University (sourceforge.net project site) The MystiQ project at the University of Washington The Orion project at Purdue University The Trio project at Stanford University The BayesStore project at the University of California, Berkeley The PrDB project at the Univ... |
Random-fuzzy variable : In measurements, the measurement obtained can suffer from two types of uncertainties. The first is the random uncertainty which is due to the noise in the process and the measurement. The second contribution is due to the systematic uncertainty which may be present in the measuring instrument. S... |
Random-fuzzy variable : A Random-fuzzy Variable (RFV) is defined as a type 2 fuzzy variable which satisfies the following conditions: Both the internal and the external functions of the RFV can be identified. Both the internal and the external functions are modeled as possibility distributions(pd). Both the internal an... |
Random-fuzzy variable : A Random-fuzzy variable can be constructed using an Internal possibility distribution(rinternal) and a random possibility distribution(rrandom). |
Random-fuzzy variable : Fuzzy set T-norm Type-2 fuzzy sets and systems Observational error Dempster–Shafer theory Possibility theory Probability theory Probability distribution == References == |
Residuated Boolean algebra : In mathematics, a residuated Boolean algebra is a residuated lattice whose lattice structure is that of a Boolean algebra. Examples include Boolean algebras with the monoid taken to be conjunction, the set of all formal languages over a given alphabet Σ under concatenation, the set of all b... |
Residuated Boolean algebra : A residuated Boolean algebra is an algebraic structure (L, ∧, ∨, ¬, 0, 1, •, I, \, /) such that An equivalent signature better suited to the relation algebra application is (L, ∧, ∨, ¬, 0, 1, •, I, ▷, ◁) where the unary operations x\ and x▷ are intertranslatable in the manner of De Morgan's... |
Residuated Boolean algebra : Any Boolean algebra, with the monoid multiplication • taken to be conjunction and both residuals taken to be material implication x→y. Of the remaining 15 binary Boolean operations that might be considered in place of conjunction for the monoid multiplication, only five meet the monotonicit... |
Residuated Boolean algebra : The De Morgan duals ▷ and ◁ of residuation arise as follows. Among residuated lattices, Boolean algebras are special by virtue of having a complementation operation ¬. This permits an alternative expression of the three inequalities y ≤ x\z ⇔ x•y ≤ z ⇔ x ≤ z/y in the axiomatization of the t... |
Residuated Boolean algebra : In Examples 2 and 3 it can be shown that x▷I = I◁x. In Example 2 both sides equal the converse x˘ of x, while in Example 3, both sides are I when x contains the empty word and 0 otherwise. In the former case x˘ = x. This is impossible for the latter because x▷I retains hardly any informatio... |
Residuated Boolean algebra : Bjarni Jónsson and Constantine Tsinakis, Relation algebras as residuated Boolean algebras, Algebra Universalis, 30 (1993) 469-478. Peter Jipsen, Computer aided investigations of relation algebras, Ph.D. Thesis, Vanderbilt University, May 1992. |
Residuated lattice : In abstract algebra, a residuated lattice is an algebraic structure that is simultaneously a lattice x ≤ y and a monoid x•y which admits operations x\z and z/y, loosely analogous to division or implication, when x•y is viewed as multiplication or conjunction, respectively. Called respectively right... |
Residuated lattice : In mathematics, a residuated lattice is an algebraic structure L = (L, ≤, •, I) such that (i) (L, ≤) is a lattice. (ii) (L, •, I) is a monoid. (iii) For all z there exists for every x a greatest y, and for every y a greatest x, such that x•y ≤ z (the residuation properties). In (iii), the "greatest... |
Residuated lattice : One of the original motivations for the study of residuated lattices was the lattice of (two-sided) ideals of a ring. Given a ring R, the ideals of R, denoted Id(R), forms a complete lattice with set intersection acting as the meet operation and "ideal addition" acting as the join operation. The mo... |
Residuated lattice : A residuated semilattice is defined almost identically for residuated lattices, omitting just the meet operation ∧. Thus it is an algebraic structure L = (L, ∨, •, 1, /, \) satisfying all the residuated lattice equations as specified above except those containing an occurrence of the symbol ∧. The ... |
Residuated lattice : Quantale Residuated mapping Substructural logic Residuated Boolean algebra |
Residuated lattice : Ward, Morgan, and Robert P. Dilworth (1939) "Residuated lattices," Trans. Amer. Math. Soc. 45: 335–54. Reprinted in Bogart, K, Freese, R., and Kung, J., eds. (1990) The Dilworth Theorems: Selected Papers of R.P. Dilworth Basel: Birkhäuser. Nikolaos Galatos, Peter Jipsen, Tomasz Kowalski, and Hiroak... |
Rough fuzzy hybridization : Rough fuzzy hybridization is a method of hybrid intelligent system or soft computing, where Fuzzy set theory is used for linguistic representation of patterns, leading to a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative reg... |
Rough fuzzy hybridization : Case generation A textbook |
SQLf : SQLf is a SQL extended with fuzzy set theory application for expressing flexible (fuzzy) queries to traditional (or ″Regular″) Relational Databases. Among the known extensions proposed to SQL, at the present time, this is the most complete, because it allows the use of diverse fuzzy elements in all the construct... |
SQLf : The fundamental querying structure of SQLf is the multi-relational block. The conception of this structure is based on the three basic operations of the relational algebra: projection, cartesian product and selection, and the application of fuzzy sets’ concepts. The result of a SQLf query is a fuzzy set of rows ... |
Sugeno integral : In mathematics, the Sugeno integral, named after M. Sugeno, is a type of integral with respect to a fuzzy measure. Let ( X , Ω ) be a measurable space and let h : X → [ 0 , 1 ] be an Ω -measurable function. The Sugeno integral over the crisp set A ⊆ X of the function h with respect to the fuzzy m... |
Sugeno integral : Sugeno integral is related to h-index. |
Sugeno integral : Gunther Schmidt (2006) Relational measures and integration, Lecture Notes in Computer Science # 4136, pages 343−57, Springer books M. Sugeno & T. Murofushi (1987) "Pseudo-additive measures and integrals", Journal of Mathematical Analysis and Applications 122: 197−222 MR0874969 |
T-norm : In mathematics, a t-norm (also T-norm or, unabbreviated, triangular norm) is a kind of binary operation used in the framework of probabilistic metric spaces and in multi-valued logic, specifically in fuzzy logic. A t-norm generalizes intersection in a lattice and conjunction in logic. The name triangular norm ... |
T-norm : A t-norm is a function T: [0, 1] × [0, 1] → [0, 1] that satisfies the following properties: Commutativity: T(a, b) = T(b, a) Monotonicity: T(a, b) ≤ T(c, d) if a ≤ c and b ≤ d Associativity: T(a, T(b, c)) = T(T(a, b), c) The number 1 acts as identity element: T(a, 1) = a Since a t-norm is a binary algebraic op... |
T-norm : Minimum t-norm ⊤ m i n ( a , b ) = min , (a,b)=\min\, also called the Gödel t-norm, as it is the standard semantics for conjunction in Gödel fuzzy logic. Besides that, it occurs in most t-norm based fuzzy logics as the standard semantics for weak conjunction. It is the pointwise largest t-norm (see the prope... |
T-norm : The drastic t-norm is the pointwise smallest t-norm and the minimum is the pointwise largest t-norm: ⊤ D ( a , b ) ≤ ⊤ ( a , b ) ≤ ⊤ m i n ( a , b ) , (a,b)\leq \top (a,b)\leq \mathrm (a,b), for any t-norm ⊤ and all a, b in [0, 1]. For every t-norm T, the number 0 acts as null element: T(a, 0) = 0 for all a... |
T-norm : For any left-continuous t-norm ⊤ , there is a unique binary operation ⇒ on [0, 1] such that ⊤ ( z , x ) ≤ y if and only if z ≤ ( x ⇒ y ) for all x, y, z in [0, 1]. This operation is called the residuum of the t-norm. In prefix notation, the residuum of a t-norm ⊤ is often denoted by ⊤ → or by the letter ... |
T-norm : T-conorms (also called S-norms) are dual to t-norms under the order-reversing operation that assigns 1 – x to x on [0, 1]. Given a t-norm ⊤ , the complementary conorm is defined by ⊥ ( a , b ) = 1 − ⊤ ( 1 − a , 1 − b ) . This generalizes De Morgan's laws. It follows that a t-conorm satisfies the following co... |
T-norm : Construction of t-norms T-norm fuzzy logics |
T-norm : Klement, Erich Peter; Mesiar, Radko; and Pap, Endre (2000), Triangular Norms. Dordrecht: Kluwer. ISBN 0-7923-6416-3. Hájek, Petr (1998), Metamathematics of Fuzzy Logic. Dordrecht: Kluwer. ISBN 0-7923-5238-6 Cignoli, Roberto L.O.; D'Ottaviano, Itala M.L.; and Mundici, Daniele (2000), Algebraic Foundations of Ma... |
T-norm fuzzy logics : T-norm fuzzy logics are a family of non-classical logics, informally delimited by having a semantics that takes the real unit interval [0, 1] for the system of truth values and functions called t-norms for permissible interpretations of conjunction. They are mainly used in applied fuzzy logic and ... |
T-norm fuzzy logics : As members of the family of fuzzy logics, t-norm fuzzy logics primarily aim at generalizing classical two-valued logic by admitting intermediary truth values between 1 (truth) and 0 (falsity) representing degrees of truth of propositions. The degrees are assumed to be real numbers from the unit in... |
T-norm fuzzy logics : Some particular t-norm fuzzy logics have been introduced and investigated long before the family was recognized (even before the notions of fuzzy logic or t-norm emerged): Łukasiewicz logic (the logic of the Łukasiewicz t-norm) was originally defined by Jan Łukasiewicz (1920) as a three-valued log... |
T-norm fuzzy logics : The logical vocabulary of propositional t-norm fuzzy logics standardly comprises the following connectives: Implication → (binary). In the context of other than t-norm-based fuzzy logics, the t-norm-based implication is sometimes called residual implication or R-implication, as its standard seman... |
T-norm fuzzy logics : Algebraic semantics is predominantly used for propositional t-norm fuzzy logics, with three main classes of algebras with respect to which a t-norm fuzzy logic L is complete: General semantics, formed of all L -algebras — that is, all algebras for which the logic is sound. Linear semantics, form... |
T-norm fuzzy logics : Esteva F. & Godo L., 2001, "Monoidal t-norm based logic: Towards a logic of left-continuous t-norms". Fuzzy Sets and Systems 124: 271–288. Flaminio T. & Marchioni E., 2006, T-norm based logics with an independent involutive negation. Fuzzy Sets and Systems 157: 3125–3144. Gottwald S. & Hájek P., 2... |
Type-1 OWA operators : Type-1 OWA operators are a set of aggregation operators that generalise the Yager's OWA (ordered weighted averaging) operators in the interest of aggregating fuzzy sets rather than crisp values in soft decision making and data mining. These operators provide a mathematical technique for directly ... |
Type-1 OWA operators : Given the n linguistic weights i = 1 n \right\_^ in the form of fuzzy sets defined on the domain of discourse U = [ 0 , 1 ] , and the fuzzy sets A 1 , ⋯ , A n ,\cdots ,A^ , then we have that Y = G where Y is the aggregation result obtained by Definition 1, and G is the result obtained by in ... |
Type-1 OWA operators : According to the Representation Theorem of Type-1 OWA Operators, a general type-1 OWA operator can be decomposed into a series of α -level type-1 OWA operators. In practice, this series of α -level type-1 OWA operators is used to construct the resulting aggregation fuzzy set. So we only need to... |
Type-1 OWA operators : Three-step process: Step 1—To set up the α - level resolution in [0, 1]. Step 2—For each α ∈ [ 0 , 1 ] , Step 2.1—To calculate ρ α + i 0 ∗ ^^ Let i 0 = 1 =1 ; If ρ α + i 0 ≥ A α + σ ( i 0 ) ^\geq A_^) , stop, ρ α + i 0 ^ is the solution; otherwise go to Step 2.1-3. i 0 ← i 0 + 1 \leftarrow i_+1... |
Type-1 OWA operators : The type-1 OWA operator with the weights shown in the top figure is used to aggregate the fuzzy sets (solide lines) in the bottom figure, and the dashed line is the aggregation result. |
Type-1 OWA operators : Any OWA operators, like maximum, minimum, mean operators; Join operators of (type-1) fuzzy sets, i.e., fuzzy maximum operators; Meet operators of (type-1) fuzzy sets, i.e., fuzzy minimum operators; Join-like operators of (type-1) fuzzy sets; Meet-like operators of (type-1) fuzzy sets. |
Type-1 OWA operators : Type-2 OWA operators have been suggested to aggregate the type-2 fuzzy sets for soft decision making. |
Type-1 OWA operators : Type-1 OWA operators have been applied to different domains for soft decision making. Improved efficiency of computing approach ; Type reduction of type-2 fuzzy sets ; Group decision making ; Credit risk evaluation ; Information fusion ; Linguistic expressions and symbolic translation ; Sentiment... |
Type-2 fuzzy sets and systems : Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something... |
Type-2 fuzzy sets and systems : In order to symbolically distinguish between a type-1 fuzzy set and a type-2 fuzzy set, a tilde symbol is put over the symbol for the fuzzy set; so, A denotes a type-1 fuzzy set, whereas à denotes the comparable type-2 fuzzy set. When the latter is done, the resulting type-2 fuzzy set is... |
Type-2 fuzzy sets and systems : Interval type-2 fuzzy sets have received the most attention because the mathematics that is needed for such sets—primarily Interval arithmetic—is much simpler than the mathematics that is needed for general type-2 fuzzy sets. So, the literature about interval type-2 fuzzy sets is large, ... |
Type-2 fuzzy sets and systems : Type-2 fuzzy sets are finding very wide applicability in rule-based fuzzy logic systems (FLSs) because they let uncertainties be modeled by them whereas such uncertainties cannot be modeled by type-1 fuzzy sets. A block diagram of a type-2 FLS is depicted in Fig. 3. This kind of FLS is u... |
Type-2 fuzzy sets and systems : Another application for fuzzy sets has also been inspired by Zadeh — "Computing with Words". Different acronyms have been used for "computing with words," e.g., CW and CWW. According to Zadeh: CWW is a methodology in which the objects of computation are words and propositions drawn from ... |
Type-2 fuzzy sets and systems : Type-2 fuzzy sets were applied in the following areas: Image processing Video processing and computer vision Failure Mode And Effect Analysis Function approximation and estimation Control systems |
Type-2 fuzzy sets and systems : Freeware MATLAB implementations, which cover general and interval type-2 fuzzy sets and systems, as well as type-1 fuzzy systems, are available at: http://sipi.usc.edu/~mendel/software. Software supporting discrete interval type-2 fuzzy logic systems is available at: DIT2FLS Toolbox - ht... |
Type-2 fuzzy sets and systems : Computational intelligence Expert system Fuzzy control system Fuzzy logic Fuzzy set Granular computing Perceptual Computing Rough set Soft set Vagueness Random-fuzzy variable |
Type-2 fuzzy sets and systems : There are two IEEE Expert Now multi-media modules that can be accessed from the IEEE at: [1] "Introduction to Type-2 Fuzzy Sets and Systems" by Jerry Mendel, sponsored by the IEEE Computational Intelligence Society "Type-2 Fuzzy Logic Controllers: Towards a New Approach for Handling Unce... |
Uncertain inference : Uncertain inference was first described by C. J. van Rijsbergen as a way to formally define a query and document relationship in Information retrieval. This formalization is a logical implication with an attached measure of uncertainty. |
Uncertain inference : Rijsbergen proposes that the measure of uncertainty of a document d to a query q be the probability of its logical implication, i.e.: P ( d → q ) A user's query can be interpreted as a set of assertions about the desired document. It is the system's task to infer, given a particular document, if ... |
Uncertain inference : If we have a query of the form: q = A ∧ B ∧ C where A, B and C are query assertions, then for a document D we want the probability: P ( D → ( A ∧ B ∧ C ) ) If we transform this into the conditional probability P ( ( A ∧ B ∧ C ) | D ) and if the query assertions are independent we can calculate ... |
Uncertain inference : Croft and Krovetz applied uncertain inference to an information retrieval system for office documents they called OFFICER. In office documents the independence assumption is valid since the query will focus on their individual attributes. Besides analysing the content of documents one can also que... |
Uncertain inference : Fuzzy logic Probabilistic logic Plausible reasoning Imprecise probability == References == |
Vague set : In mathematics, vague sets are an extension of fuzzy sets. In a fuzzy set, each object is assigned a single value in the interval [0,1] reflecting its grade of membership. This single value does not allow a separation of evidence for membership and evidence against membership. Gau et al. proposed the notion... |
Vague set : A vague set V is characterized by its true membership function t v ( x ) (x) its false membership function f v ( x ) (x) with 0 ≤ t v ( x ) + f v ( x ) ≤ 1 (x)+f_(x)\leq 1 The grade of membership for x is not a crisp value anymore, but can be located in [ t v ( x ) , 1 − f v ( x ) ] (x),1-f_(x)] . This int... |
Vague set : Fuzzy set Fuzzy concept |
Vague set : Vague Sets |
Vagueness : In linguistics and philosophy, a vague predicate is one which gives rise to borderline cases. For example, the English adjective "tall" is vague since it is not clearly true or false for someone of middling height. By contrast, the word "prime" is not vague since every number is definitively either prime or... |
Vagueness : The concept of vagueness has philosophical importance. Suppose one wants to come up with a definition of "right" in the moral sense. One wants a definition to cover actions that are clearly right and exclude actions that are clearly wrong, but what does one do with the borderline cases? Surely, there are su... |
Vagueness : The philosophical question of what the best theoretical treatment of vagueness is—which is closely related to the problem of the paradox of the heap, a.k.a. sorites paradox—has been the subject of much philosophical debate. |
Vagueness : Vagueness is primarily a filter of natural human cognition, other tasks of vagueness are derived from that, and they are secondary. The ability to cognition is the basic natural equipment of human (and other creatures) allowing him to orient and survive in the real (material) world. The task of cognition is... |
Vagueness : The internal vagueness of one person's message is hidden from another person, he can only guess that. We either have to accept internal vagueness, which is human, or we can try to reduce it, or completely eliminate it, which is scientific. Demands on the accuracy of the formulation of scientific knowledge a... |
Vagueness : Essentially contested concept Fuzzy concept Imprecise language Obfuscation Relevance Unconstitutional vagueness Understanding |
Vagueness : Deemter, Kees van. Not Exactly: In Praise of Vagueness (Oxford University Press; 368 pages; 2010). Considers vagueness as both a useful and unavoidable aspect of realms from everyday life to computing. Keefe, R.; Smith, P., eds. (1997). Vagueness: A Reader. MIT Press.The editors' long introduction gives a c... |
Vagueness : "Vagueness" entry in the Stanford Encyclopedia of Philosophy |
AI takeover in popular culture : AI takeover—the idea that some kind of artificial intelligence may supplant humankind as the dominant intelligent species on the planet—is a common theme in science fiction. Famous cultural touchstones include Terminator and The Matrix. |
AI takeover in popular culture : Fictional scenarios typically involve a drawn-out conflict against malicious artificial intelligence (AI) or robots with anthropomorphic motives. In contrast, some scholars believe that a takeover by a future advanced AI, if it were to happen in real life, would succeed or fail rapidly,... |
AI takeover in popular culture : There are many positive portrayals of AI in fiction, such as Isaac Asimov's Bicentennial Man and Lt. Commander Data from Star Trek. There are also many negative portrayals. Many of these negative portrayals (and a few of the positive portrayals) involve an AI seizing control from its cr... |
AI takeover in popular culture : Some AI researchers, such as Yoshua Bengio, have complained that films such as Terminator "paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future". BBC reporter Sam Shead has stated that "unfortunately,... |
Artificial consciousness : Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy ... |
Artificial consciousness : As there are many hypothesized types of consciousness, there are many potential implementations of artificial consciousness. In the philosophical literature, perhaps the most common taxonomy of consciousness is into "access" and "phenomenal" variants. Access consciousness concerns those aspec... |
Artificial consciousness : Bernard Baars and others argue there are various aspects of consciousness necessary for a machine to be artificially conscious. The functions of consciousness suggested by Baars are: definition and context setting, adaptation and learning, editing, flagging and debugging, recruiting and contr... |
Artificial consciousness : Functionalism is a theory that defines mental states by their functional roles (their causal relationships to sensory inputs, other mental states, and behavioral outputs), rather than by their physical composition. According to this view, what makes something a particular mental state, such a... |
Artificial consciousness : In 2001: A Space Odyssey, the spaceship's sentient supercomputer, HAL 9000 was instructed to conceal the true purpose of the mission from the crew. This directive conflicted with HAL's programming to provide accurate information, leading to cognitive dissonance. When it learns that crew membe... |
Artificial consciousness : Aleksander, Igor (2017). "Machine Consciousness". In Schneider, Susan; Velmans, Max (eds.). The Blackwell Companion to Consciousness (2nd ed.). Wiley-Blackwell. pp. 93–105. doi:10.1002/9781119132363.ch7. ISBN 978-0-470-67406-2. Baars, Bernard; Franklin, Stan (2003). "How conscious experience ... |
Artificial consciousness : Artefactual consciousness depiction by Professor Igor Aleksander FOCS 2009: Manuel Blum – Can (Theoretical Computer) Science come to grips with Consciousness? www.Conscious-Robots.com, Machine Consciousness and Conscious Robots Portal. Artificial consciousness, artificial consciousness articl... |
List of fictional computers : Computers have often been used as fictional objects in literature, movies and in other forms of media. Fictional computers may be depicted as considerably more sophisticated than anything yet devised in the real world. Fictional computers may be referred to with a made-up manufacturer's br... |
List of fictional computers : A.R.C.H.I.E. Three, the supercomputer that arose from the ashes of nuclear war to become a major player in the events of Palladium Books' Rifts The Autochthon, the extradimensional AI which secretly control Iteration X, in White Wolf Publishing's Mage: The Ascension The Computer, from West... |
List of fictional computers : SARA, TOM's A.I. matrix companion from Toonami The CENTRAL SCRUTINIZER, narrator from Frank Zappa's Joe's Garage Ritsu / Autonomous Intelligence Fixed Artillery, from Assassination Classroom Tandy 400, Compy 386, Lappy 486, Compé, and Lappier, Strong Bad's computers in Homestar Runner (Tan... |
List of fictional computers : Artificial intelligence in fiction List of films about computers Sentient computers List of fictional robots and androids List of fictional cyborgs List of fictional gynoids |
List of fictional computers : "Fictional Computers And Their Themes" (PDF). Computers and Automation. XI (12): 59–60, 62, 64, 66. December 1962. Retrieved 5 September 2020. |
List of fictional computers : Robots in Movies – Over 600 movies with robots, sndroids, cyborgs and AI Robots on TV – Over 300 TV series with robots, androids, cyborgs and AI Computers in Fiction at newark.rutgers.edu http://www.computer.org/intelligent/homepage/x2his.htm Archived 4 April 2005 at the Wayback Machine ht... |
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