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Hopfield network. A Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982.

Hopfield network

Hopfield nets serve as content-addressable memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but convergence to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum) can occur. Artificial neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.

Artificial neural network

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Computability theory. Computability theory, also called recursion theory, is a branch of mathematical logic, of computer science, and of the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees.

Computability theory

The basic questions addressed by recursion theory are "What does it mean for a function on the natural numbers to be computable? " and "How can noncomputable functions be classified into a hierarchy based on their level of noncomputability? ". The answers to these questions have led to a rich theory that is still being actively researched.

The field has since grown to include the study of generalized computability and definability. Invention of the central combinatorial object of recursion theory, namely the Universal Turing Machine, predates and predetermines the invention of modern computers. Recursion theory overlaps with proof theory, effective descriptive set theory, model theory, and abstract algebra. Computability theory. Introduction to Neural Networks for Java, Second Edition. List of artificial intelligence projects. The following is a list of current and past, nonclassified notable artificial intelligence projects. Specialized projects[edit] Brain-inspired[edit] Cognitive architectures[edit] Games[edit] Knowledge and reasoning[edit] Motion and manipulation[edit] Natural language processing[edit] AIML, an XML dialect for creating natural language software agents.Artificial Linguistic Internet Computer Entity (A.L.I.C.E.), an award-winning natural language processing chatterbot.Cleverbot,successor to Jabberwacky, now with 170m lines of conversation, Deep Context, fuzziness and parallel processing.Cleverbot learns from around 2 million user interactions per month.ELIZA, a famous 1966 computer program by Joseph Weizenbaum, which parodied person-centered therapy.InfoTame, a text analysis search engine originally developed by the KGB for sorting communications intercepts.Jabberwacky, a chatterbot by Rollo Carpenter, aiming to simulate a natural human chat.KAR-Talk, a chatterbot by I.

Grammatical Terms and Definitions. What Is a Complex Sentence? A complex sentence has one independent clause and at least one dependent clause.

What Is a Complex Sentence?

An independent clause (unlike a dependent clause) can stand alone as a sentence. Examples of Complex Sentences Below are examples of complex sentences. In each example, the independent clause is shaded. The dependent clause is unshaded. The Four Types of Sentence Structure. Algorithm. Flow chart of an algorithm (Euclid's algorithm) for calculating the greatest common divisor (g.c.d.) of two numbers a and b in locations named A and B.

Algorithm

The algorithm proceeds by successive subtractions in two loops: IF the test B ≥ A yields "yes" (or true) (more accurately the numberb in location B is greater than or equal to the numbera in location A) THEN, the algorithm specifies B ← B − A (meaning the number b − a replaces the old b). Similarly, IF A > B, THEN A ← A − B. The process terminates when (the contents of) B is 0, yielding the g.c.d. in A. Algorithm. Abstract syntax tree.

An abstract syntax tree for the following code for the Euclidean algorithm: while b ≠ 0.

Abstract syntax tree

Cognitive dimensions of notations. Cognitive dimensions or cognitive dimensions of notations[1][2] are design principles for notations, user interfaces and programming language design, described by researchers Thomas R.G.

Cognitive dimensions of notations

Green and Marian Petre. The dimensions can be used to evaluate the usability of an existing information artifact, or as heuristics to guide the design of a new one. Cognitive dimensions are designed to provide a lightweight approach to analysis of a design quality, rather than an in-depth, detailed description. They provide a common vocabulary for discussing many factors in notation, UI or programming language design. Also, cognitive dimensions help in exploring the space of possible designs through design maneuvers, changes intended to improve the design along one dimension.

Cognitive dimensions of notations. Genetic Algorithms (Java) I needed to add a genetic algorithm to a network optimization program that I was writing.

Genetic Algorithms (Java)

It needed to be in Java, since that is a language that the main network graphing engine (Gephi) that I use is written in. Surprisingly RapidMinder or KMine did not have any implementations available. Genetic Algorithms (Java) A* search algorithm. A* is a set of steps (an algorithm) that computers can use to figure out how to get somewhere fast between two places.

A* search algorithm

If you have a list of locations, and how hard it is to get from one straight to the other, using A* can quickly tell you the fastest way. It's related to Dijkstra's algorithm, but makes smart guesses so that it doesn't spend as long trying slow ways. It's a good series of steps if you only want the path between two places. If you're going to ask for many paths from the same map, then there are faster ways, that find all the answers at once, like the Floyd–Warshall algorithm. Artificial Life on the Web Java Alife Experiments and Artist 3D Dolls. A web oriented artificial life site: alife, genetic algorithms and cellular automata experiments written in cross platform web languages (java, tcl/tk), with free source code My Topics of Interest My main topics of interest are Evolution, Artificial Life and Computers.

Artificial Life on the Web Java Alife Experiments and Artist 3D Dolls

I became fascinated by these three ideas: The idea that complex, sophisticated, adaptive solutions can be generated by automatic, blind, knowledge-lacking mechanisms (Evolution). The idea that complex systems, such as life, are actually the emergent behaviors of systems with many elements that operate according to simple, local rules (Artificial Life). The idea that a personal computer can be an important scientific laboratory tool, and that new insights and new knowledge can (potentially) be achieved by using inexpensive equipment for conducting scientific experiments from one's home. Artificial Life and Java Floys - Social, Territorial, Evolving Java Alife Animals. Designing Unsupervised Hierarchical Fuzzy Logic Systems (Artificial Intelligence) Systems such as robotic systems and systems with large input-output data tend to be difficult to model using mathematical techniques. These systems have typically high dimensionality and have degrees of uncertainty in many parameters.Artificial intelligence techniques such as neural networks, fuzzy logic, genetic algorithms and evolutionary algorithms have created new opportunities to solve complex systems.

Application of fuzzy logic [Bai, Y., Zhuang H. and Wang, D. (2006)] in particular, to model and solve industrial problems is now wide spread and has universal acceptance. Fuzzy modelling or fuzzy identification has numerous practical applications in control, prediction and inference. It has been found useful when the system is either difficult to predict and or difficult to model by conventional methods. The Dempster-Shafer Theory (Artificial Intelligence) The initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered through distributions of random sets.

More furtive comparison has been with the traditional Bayesian theory, where D-S theory has been considered a generalisation of it (Schubert, 1994). Cobb and Shenoy (2003) direct its attention to the comparison of D-S theory and the Bayesian for-mulisation. Their conclusions are that they have the same expressive power, but that one technique cannot simply take the role of the other. The association with artificial intelligence (AI) is clearly outlined in Smets (1990), who at the time, acknowledged the AI community has started to show interest for what they call the Dempster-Shafer model.

Main Thrust. Designing Unsupervised Hierarchical Fuzzy Logic Systems (Artificial Intelligence) Designing Unsupervised Hierarchical Fuzzy Logic Systems (Artificial Intelligence) Sense and reference. Metalogic. Metalogic is the study of the metatheory of logic. Wheras logic studies how logical systems can be used to construct valid and sound arguments, metalogic studies the properties of logical systems.[1] Logic concerns the truths that may be derived using a logical system; metalogic concerns the truths that may be derived about the languages and systems that are used to express truths.

Overview[edit] Formal language[edit] Metaknowledge. Metaknowledge or meta-knowledge is knowledge about a preselected knowledge. For the reason of different definitions of knowledge in the subject matter literature, meta-information is or is not included in meta-knowledge. Detailed cognitive, systemic and epistemic study of human knowledge requires a distinguishing of these concepts. but in the common language knowledge includes information, and, for example, bibliographic data are considered as a meta-knowledge.

Metaknowledge may be automatically harvested from electronic publication archives, to reveal patterns in research, relationships between researchers and institutions and to identify contradictory results.[2] Genetic enhancement of learning and memory : the NMDA receptor NR2B. Genetic enhancement of learning and memory : the NMDA receptor NR2B.