An output signal (also called nerve impulse) of a biological neuron that does not lose strength over long distances. When an action potential occurs, the neuron is said to "fire an impulse". [nn]

A mathematical function that maps the net input of a neuron to its output. Commonly used activation functions are: step, sign, linear and sigmoid. Also referred to as Transfer function. [nn]

A learning rate adjusted according to the change of error during training. If the error at the current epoch exceeds the previous value by more than a predefined ratio, the learning rate is decreased. However, if the error is less than the previous one, the learning rate is increased. The use of an adaptive learning rate accelerates learning in a multilayer perceptron. [nn]

A fuzzy set obtained through aggregation. [fl]

The third step in fuzzy inference; the process of combining clipped or scaled consequent membership functions of all fuzzy rules into a single fuzzy set for each output variable. [fl]

A set of step-by-step instructions for solving a problem.

A logical operator; when used in a production rule, it implies that all antecedents joined with AND must be true for the rule consequent to be true. [es]

A conditional statement in the IF part of a rule. Also referred to as Premise. [es]

An ark (also known as "part-whole") that associates subclasses representing components with a superclass representing the whole. For example, an engine is a-part-of a car. [es]

Reasoning that does not require a precise matching between the IF part of a production rule with the data in the database. [es]

A directed labelled link between nodes in a semantic network that indicates the nature of the connection between adjacent nodes. The most common arcs are is-a and a-part-of. [es]

see Topology.

An information-processing paradigm inspired by the structure and functions of the human brain. An ANN consists of a number of simple and highly interconnected processors, called neurons, which are analogous to the biological neurons in the brain. The neurons are connected by weighted links that pass signals from one neuron to another. While in a biological neural network, learning involves adjustments to the synapses, ANNs learn through repeated adjustments of the weights. These weights store the knowledge needed to solve specific problems. [nn]

The field of computer science concerned with developing machines that behave in a way that would be considered intelligent if observed in humans.

A fact derived during reasoning. [es]

The type of memory that allows us to associate one thing with another. For example, we can recall a complete sensory experience, including sounds and scenes, when we hear only a few bars of music. We can also recognise a familiar face even in an unfamiliar environment. An associative ANN recalls the closest "stored" training pattern when presented with a similar input pattern. The Hopfield network is an example of the associative ANN. [nn]

A property of an object. For example, the object "computer" might have such attributes as "model", "processor", "memory" and "cost". [es]

A single long branch of a biological neuron that carries the output signal (action potential) from the cell. An axon may be as long as a meter. In an ANN, an axon is modelled by the neuron’s output. [nn]

An inference technique that starts with a hypothetical solution (a goal) and works backward, matching rules from the rule base with facts from the database until the goal is either verified or proven wrong. Also referred to as Goal-driven reasoning. [es]

see Back-propagation algorithm. [nn]

The most popular method of supervised learning. The algorithm has two phases. First, a training input pattern is presented to the input layer. The network propagates the input pattern from layer to layer until the output pattern is generated by the output layer. If this pattern is different from the desired output, an error is calculated and then propagated backwards through the network from the output layer to the input layer. The weights are modified as the error is propagated. Also referred to as Back-propagation. [nn]

A statistical approach to uncertainty management in expert systems that propagates uncertainties throughout the system based on a Bayesian rule of evidence. [es]

A statistical method for updating the probabilities attached to certain facts in the light of new evidence. [es]

A class of neural networks that emulates characteristics of associative memory; proposed by Bart Kosko in the 1980s. The BAM associates patterns from one set to patterns from another set, and vice versa. Its basic architecture consists of two fully connected layers - an input layer and an output layer. [nn]

Bit

A binary digit. The smallest unit of information. Data stored in a computer is composed of bits. [ke]

A representation of an image by rows and columns of dots. Bit maps can be stored, displayed and printed by a computer. Optical scanners are used to transform text or pictures on paper into bit maps. The scanner processes the image by dividing it into hundreds of pixels per inch and representing each pixel by either 1 or 0. [ke]

A model that is opaque to its user; although the model can produce correct results, its internal relationships are not known. An example of a black-box is a neural network. To understand the relationships between outputs and inputs of a block-box, sensitivity analysis can be used. [ke]

A system of logic based on Boolean algebra, named after George Boole. It deals with two truth values: "true" and "false". The Boolean conditions of true and false are often represented by 0 for "false" and 1 for "true".

A connection between nodes in a decision tree. [dm]

A group of genes that gives a chromosome a high fitness. According to the building block hypothesis, an optimal solution can be found by joining several building blocks together in a single chromosome. [ec]

A set of eight bits that represents the smallest addressable item of information in a modern computer. The information in a byte is equivalent to a letter in a word. One gigabyte is about 1,000,000,000 (230 or 1,073,741,824) bytes, approximately equal to 1000 novels. [ke]

A general-purpose programming language, originally developed at Bell Labs along with the UNIX operating system.

An object-oriented extension of C.

A tool for data mining that uses decision trees. CART provides a set of rules that can be applied to a new data set for predicting outcomes. CART segments data records by creating binary splits. [dm]

The data that fits into a small number of discrete categories. For example, gender (male or female) or marital status (single, divorced, married or widowed). [ke]

A defuzzification method that finds the point, called the centroid or centre of gravity, where a vertical line would slice the aggregate set into two equal masses. [fl]

A number assigned to a fact or a rule to indicate the certainty or confidence one has that this fact or rule is valid. Also referred to as Confidence factor. [es]

A theory for managing uncertainties in expert systems based on inexact reasoning. It uses certainty factors to represent the level of belief in a hypothesis given that a particular event has been observed. [es]

see Offspring. [ec]

In a decision tree, a child is a node produced by splitting the data of a node located at the preceding hierarchical level of the tree. A child node holds a subset of the data contained in its parent. [dm]

A string of genes that represent an individual. [ec]

A group of objects with common attributes. Animal, person, car and computer are all classes. [es]

A frame that represents a class. [es]

A common method of correlating the consequent of a fuzzy rule with the truth value of the rule antecedent. The method is based on cutting the consequent membership function at the level of the antecedent truth. Since the top of the membership function is sliced, the clipped fuzzy set loses some information. [fl]

Creating an offspring that is an exact copy of a parent. [ec]

The process of transforming information from one scheme of representation to another. [ec]

The interdisciplinary study of how knowledge is acquired and used. Its contributing disciplines include artificial intelligence, psychology, linguistics, philosophy, neuroscience, and education. Also, the study of intelligence and intelligent systems, with reference to intelligent behaviour as computation.

A general knowledge of how to solve real-world problems, usually obtained through practical experience.[ke]

Unsupervised learning in which neurons compete among themselves such that only one neuron will respond to a particular input pattern. The neuron that wins the "competition" is called the winner-takes-all neuron. Kohonen self-organising feature maps are an example of an ANN with competitive learning. [nn]

In classical set theory, the complement of set A is the set of elements that are not members of A. In the fuzzy set theory, the complement of a set is an opposite of this set. [fl]

see Certainty factor. [es]

A state in which two or more production rules match the data in the database, but only one rule can actually be fired in a given cycle. [es]

A method for choosing which production rule to fire when more than one rule can be fired in a given cycle. [es]

The logical operator AND that joins together two antecedents in a production rule. [es]

A link from one neuron to another to transfer signals. Also referred to as synapse, which is often associated with the weight that determines the strength of the transferred signal. [nn]

A conclusion or action in the IF part of a rule. [es]

The data that takes an infinite number of possible values on some interval. Examples of continuous data include height, weight, household income, the living area of a house. Continuous variables are usually measurements, and do not have to be integers. [ke]

An ANN is said to have converged when the error has reached a pre-set threshold indicating that the network has learned the task. [nn]

A tendency of individuals in the population to be the same. A genetic algorithm is said to have converged when a solution has been reached. [ec]

A reproduction operator that creates a new chromosome by exchanging parts of two existing chromosomes. [ec]

A number between zero and one that indicates the probability of two chromosomes crossing over. [ec]

Charles Darwin’s theory that states that evolution occurs through natural selection, coupled with random changes of inheritable characteristics. [ec]

Facts, measurements, or observations. Also, a symbolic representation of facts, measurements, or observations. Data is what we collect and store.

A collection of structured data. Database is the basic component of an expert system. [es]

see Forward chaining. [es]

The process of detecting and correcting obvious errors and replacing missing data in a database. Also referred to as Data cleansing. [dm]

see Data cleaning. [dm]

The extraction of knowledge from data. Also, the exploration and analysis of large amounts of data in order to discover meaningful patterns and rules. The ultimate goal of data mining is to discover knowledge. [dm]

A set of values corresponding to the attributes of a single object. A data record is a row in a database. Also referred to as Record. [dm]

The graphical representation of data that helps the user in understanding the structure and meaning of the information contained in the data. Also referred to as Visualisation. [dm]

A large database that includes millions, even billions, of data records designed to support decision-making in organisations. It is structured for rapid on-line queries and managerial summaries.[dm]

A graphical representation of a data set that describes the data by tree-like structures. A decision tree consists of nodes, branches and leaves. The tree always starts from the root node and grows down by splitting the data at each level into new nodes. Decision trees are particularly good at solving classification problems. Their main advantage is data visualisation. [dm]

An interactive computer-based system designed to help a person or a group of people to make decisions in a specific domain. [es]

Reasoning from the general to the specific. [es]

The last step in fuzzy inference; the process of converting a combined output of fuzzy rules into a crisp (numerical) value. The input for the defuzzification process is the aggregate set and the output is a single number. [fl]

A numerical value between 0 and 1 that represents the degree to which an element belongs to a particular set. Also referred to as Membership value. [fl]

A procedure for updating weights in a perceptron during training. The delta rule determines the weight correction by multiplying the neuron’s input with the error and the learning rate. [nn]

A procedure that is attached to a slot and executed if the slot value is changed or needed. A demon usually has an IF-THEN structure. Demon and method are often used as synonyms. [es]

A rule-based expert system developed at Stanford University in the late 1960s for analysing chemicals, based on the mass spectral data provided by a mass spectrometer. DENDRAL marked a major "paradigm shift" in AI: a shift from general-purpose, knowledge-sparse methods to domain-specific, knowledge-intensive techniques. [es]

A branch of a biological neuron that transfers information from one part of a cell to another. Dendrites typically serve an input function for the cell, although many dendrites also have output functions. In an ANN, dendrites are modelled by inputs to a neuron. [nn]

A mathematical model that postulates exact relationships between objects (no random variables are recognised). Given a set of input data, the deterministic model determines its output with complete certainty. [es]

The data that takes only a finite number of distinct values. Discrete data are usually (but not necessarily) counts. Examples of discrete data include the number of children in a family, the number of bedrooms in a house, the number of masts of a sailing vessel. [ke]

The logical operator OR that joins together two antecedents in a production rule. [es]

A relatively narrow problem area. For example, diagnosing blood diseases within the medical diagnostics field. Expert systems work in well-focused specialised domains. [es]

see Expert. [es]

Empty MYCIN, an expert system shell developed at Stanford University in the late 1970s. It has all features of the MYCIN system except the knowledge of infectious blood diseases. EMYCIN is used to develop diagnostic expert systems. [es]

see User. [es]

The presentation of the entire training set to an ANN during training. [nn]

The difference between the actual and desired outputs in an ANN with supervised learning. [nn]

A series of genetic changes by which a living organism acquires characteristics that distinguish it from other organisms. [ec]

A numerical optimisation procedure similar to a focused Monte Carlo search. Unlike genetic algorithms, evolution strategies use only a mutation operator, and do not require a problem to be represented in a coded form. Evolution strategies are used for solving technical optimisation problems when no analytical objective function is available, and no conventional optimisation method exists. [ec]

Computational models used for simulating evolution on a computer. The field of evolutionary computation includes genetic algorithms, evolution strategies and genetic programming. [ec]

A problem-solving technique in which every possible solution is examined until an acceptable one is found. [es]

A person who has deep knowledge in the form of facts and rules and strong practical experience in a particular domain. Also referred to as Domain expert. [es]

A computer program capable of performing at the level of a human expert in a narrow domain. Expert systems have five basic components: the knowledge base, the database, the inference engine, the explanation facilities and the user interface. [es]

A skeleton expert system with the knowledge removed. Also referred to as Shell. [es]

A basic component of an expert system that enables the user to query the expert system about how it reached a particular conclusion and why it needs a specific fact to do so. [es]

A means of providing extended knowledge about an attribute of a frame. Facets are used to establish the attribute value, control the user queries, and tell the inference engine how to process the attribute. [es]

A statement that has the property of being either true or false. [es]

A topology of an ANN in which neurons have feedback loops from their outputs to their inputs. An example of a feedback network is the Hopfield network. Also referred to as Recurrent network. [nn]

A topology of an ANN in which neurons in one layer are connected to the neurons in the next layer. The input signals are propagated in a forward direction on a layer-by-layer basis. An example of a feedforward network is a multilayer perceptron. [nn]

A space allocated in a database for a particular attribute. (In a spreadsheet, fields are called cells). A tax form, for example, contains a number of fields: your name and address, tax file number, taxable income, etc. Every field in a database has a name, called the field name. [dm]

The process of executing a production rule, or more precisely, executing the THEN part of a rule when its IF part is true. [es]

The ability of a living organism to survive and reproduce in a specific environment. Also, a value associated with a chromosome that assigns a relative merit to that chromosome. [ec]

A mathematical function used for calculating the fitness of a chromosome. [ec]

An inference technique that starts from the known data and works forward, matching the facts from the database with production rules from the rule base until no further rules can be fired. Also referred to as Data-driven reasoning. [es]

A data structure with typical knowledge about a particular object. Frames are used to represent knowledge in a frame-based expert system. [es]

An expert system in which frames represent a major source of knowledge, and both methods and demons are used to add actions to the frames. In frame-based systems, production rules play an auxiliary role. [es]

The first step in fuzzy inference; the process of mapping crisp (numerical) inputs into degrees to which these inputs belong to the respective fuzzy sets. [fl]

An expert system that uses fuzzy logic instead of Boolean logic. A fuzzy expert system is a collection of fuzzy rules and membership functions that are used to reason about data. Unlike conventional expert systems, which use symbolic reasoning, fuzzy expert systems are oriented towards numerical processing. [fl]

The process of reasoning based on fuzzy logic. Fuzzy inference includes four steps: fuzzification of the input variables, rule evaluation, aggregation of the rule outputs and defuzzification. [fl]

A system of logic developed for representing conditions that cannot be easily described by the binary terms "true" and "false". The concept was introduced by Lotfi Zadeh in 1965. Unlike Boolean logic, fuzzy logic is multi-valued and handles the concept of partial truth (truth values between "completely true" and "completely false"). Also referred to as Fuzzy set theory. [fl]

A conditional statement in the form: IF x is A THEN y is B, where x and y are linguistic variables, and A and B are linguistic values determined by fuzzy sets. [fl]

A set with fuzzy boundaries, such as "short", "average" or "tall" for men’s height. To represent a fuzzy set in a computer, we express it as a function and then map the elements of the set to their degree of membership. [fl]

see Fuzzy logic. [fl]

A fuzzy set with a membership function equal to unity at a single point on the universe of discourse and zero everywhere else. Also referred to as Singleton. [fl]

A quantity that can take on linguistic values. For example, the fuzzy variable "temperature", might have values such as "hot", "medium" and "cold".

A basic unit of a chromosome that controls the development of a particular feature of a living organism. In Holland’s chromosome, a gene is represented by either 0 or 1. [ec]

An early AI system that attempted to simulate human methods of problem solving. The GPS was the first attempt to separate the problem-solving technique from the data. However, the program was based on the general-purpose search mechanism. This approach, now referred to as a weak method, applied weak information about the problem domain, and resulted in weak performance of the program in solving real-world problems. [es]

One iteration of a genetic algorithm. [ec]

The ability of an ANN to produce correct results from data on which it has not been trained. [nn]

A type of evolutionary computation inspired by Darwin’s theory of evolution. A genetic algorithm generates a population of possible solutions encoded as chromosomes, evaluates their fitness, and creates a new population by applying genetic operators – crossover and mutation. By repeating this process over many generations, the genetic algorithm breeds an optimal solution to the problem. [ec]

An application of genetic algorithms to computer programs. Genetic programming is most easily implemented where the programming language permits a program to be manipulated as data and the newly created data to be executed as a program. This is one of the reasons why LISP is used as the main language for genetic programming. [ec]

An operator in genetic algorithms or genetic programming, which acts upon the chromosome in order to produce a new individual. Genetic operators include crossover and mutation. [ec]

The lowest value of a function over the entire range of its input parameters. During training, the weights of an ANN are adjusted to find the global minimum of the error function. [nn]

Finding the true optimum in the entire search space. [ec]

A hypothesis that an expert system attempts to prove. [es]

see Backward chaining. [es]

An activation function represented by the step and sign functions. Also referred to as Hard limiter. [nn]

see Hard limit activation function. [nn]

The learning law introduced by Donald Hebb in the late 1940s; it states that if neuron i is near enough to excite neuron j and repeatedly participates in its activation, the synaptic connection between these two neurons is strengthened and neuron j becomes more sensitive to stimuli from neuron i. This law provides the basis for unsupervised learning. [nn]

Unsupervised learning that relates a change in the weight of the synaptic connection between a pair of neurons to a product of the incoming and outgoing signals. [nn]

A qualifier of a fuzzy set used to modify its shape. Hedges include adverbs such as "very", "somewhat", "quite", "more or less" and "slightly". They perform mathematical operations of concentration by reducing the degree of membership of fuzzy elements (e.g., very tall men), dilation by increasing the degree of membership (e.g., more or less tall men) and intensification by increasing the degree of membership above 0.5 and decreasing those below 0.5 (e.g., indeed tall men). [fl]

A strategy that can be applied to complex problems; it usually – but not always – yields a correct solution. Heuristics, which are developed from years of experience, are often used to reduce complex problem solving to more simple operations based on judgment. Heuristics are often expressed as rules of thumb. [es]

A search technique that applies heuristics to guide the reasoning, and thus reduce the search space for a solution. [es]

A layer of neurons between the input and output layers; called "hidden" because neurons in this layer cannot be observed through the input/output behaviour of the neural network. There is no obvious way to know what the desired output of the hidden layer should be. [nn]

A neuron in the hidden layer. [nn]

A single-layer feedback neural network. In the Hopfield network, the output of each neuron is fed back to the inputs of all other neurons (there is no self-feedback). The Hopfield network usually uses McCulloch and Pitts neurons with the sign activation function. The Hopfield network attempts to emulate characteristics of the associative memory. [nn]

A system that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. [ke]

A statement that is subject to proof. Also, a goal in expert systems that use backward chaining. [es]

A single member of a population. [ec]

Reasoning from the specific to the general. [es]

The sequence of steps that indicates how an expert system applies rules from the rule base to reach a conclusion. [es]

A basic component of an expert system that carries out reasoning whereby the expert system reaches a solution. It matches the rules provided in the rule base with the facts contained in the database. Also referred to as Interpreter. [es]

The technique used by the inference engine to direct search and reasoning in an expert system. There are two principal techniques: forward chaining and backward chaining. [es]

The process by which all characteristics of a class-frame are assumed by the instance-frame. Inheritance is an essential feature of frame-based systems. A common use of inheritance is to impose default features on all instance-frames. [es]

The first step of the training algorithm that sets weights and thresholds to their initial values. [nn]

The first layer of neurons in an ANN. The input layer accepts input signals from the outside world and redistributes them to neurons in the next layer. The input layer rarely includes computing neurons and does not process input patterns. [nn]

A neuron in the input layer. [nn]

A specific object from a class. For example, class "computer" may have instances IBM Aptiva S35 and IBM Aptiva S9C. In frame-based expert systems, all characteristics of a class are inherited by its instances. [es]

A member of the schema. For example, chromosomes and are the instances of the schema . [ec]

A frame that represents an instance. [es]

The process of assigning a specific value to a variable. For example, "August" is an instantiation of the object "month". [es]

The ability to learn and understand, to solve problems and to make decisions. A machine is thought intelligent if it can achieve human-level performance in some cognitive task.

see Inference engine. [es]

In classical set theory, an intersection between two sets contains elements shared by these sets. For example, the intersection of tall men and fat men contains all men who are tall and fat. In fuzzy set theory, an element may partly belong to both sets, and the intersection is the lowest membership value of the element in both sets. [fl]

An ark (also known as "a-kind-of") that associates a superclass with its subclasses in a frame-based expert system. For example, if car is-a vehicle, then car represents a subclass of more general superclass vehicle. Each subclass inherits all features of the superclass.

A theoretical or practical understanding of a subject. Knowledge is what helps us to make informed decisions.

The process of acquiring, studying and organising knowledge, so that it can be used in a knowledge-based system. [ke]

A basic component of an expert system that contains knowledge about a specific domain. [es]

A system that uses stored knowledge for solving problems in a specific domain. A knowledge-based system is usually evaluated by comparing its performance with the performance of a human expert. [es]

A person who designs, builds and tests a knowledge-based system. The knowledge engineer captures the knowledge from the domain expert, establishes reasoning methods and chooses the development software. [ke]

The process of building a knowledge-based system. There are six main steps: assess the problem; acquire data and knowledge; develop a prototype system; develop a complete system; evaluate and revise the system; integrate and maintain the system. [ke]

The process of structuring knowledge to be stored in a knowledge-based system. In AI, production rules are the most common type of knowledge representation. [ke]

A special class of ANNs with competitive learning introduced by Teuvo Kohonen in the late 1980s. The Kohonen map consists of a single layer of computation neurons with two types of connections: forward connections from the neurons in the input layer to the neurons in the output layer, and lateral connections between neurons in the output layer. The lateral connections are used to create a competition between neurons. A neuron learns by shifting its weights from inactive connections to active ones. Only the winning neuron and its neighbourhood are allowed to learn. [nn]

A group of neurons that have a specific function and are processed as a whole. For example, a multilayer perceptron has at least three layers: an input layer, an output layer and one or more hidden layers. [nn]

A bottom-most node of a decision tree; a node without children. Also referred to as a Terminal node. [dm]

The process by which weights in an ANN are adjusted to achieve some desired behaviour of the network. Also referred to as Training. [nn]

A positive number less than unity that controls the amount of changes to the weights in the ANN from one iteration to the next. The learning rate directly affects the speed of network training. [nn]

A procedure for modifying weights during training in an ANN. [nn]

An activation function that produces an output equal to the net input of a neuron. Neurons with the linear activation function are often used for linear approximation. [nn]

A variable that can have values that are language elements, such as words and phrases. In fuzzy logic, terms linguistic variable and fuzzy variable are synonyms. [fl]

A language element that can be assumed by a fuzzy variable. For example, the fuzzy variable "income" might assume such linguistic values as "very low", "low", "medium", "high" and "very high". Linguistic values are defined by membership functions. [fl]

One of the oldest high-level programming languages. LISP, which was developed by John McCarthy in the late 1950s, has become a standard language for artificial intelligence.

The minimum value of a function over a limited range of its input parameters. If a local minimum is encountered during training, the desired behaviour of an ANN may never be achieved. The usual method of getting out of a local minimum is to randomise the weights and continue training. [nn]

An adaptive mechanism that enable computers to learn from experience, learn by example and learn by analogy. Learning capabilities improve the performance of an intelligent system over time. Machine learning is the basis of adaptive systems. The most popular approaches to machine learning are artificial neural networks and genetic algorithms.

The process of modifying the data before it is applied to the input layer of an ANN. [nn]

A neuron model proposed by Warren McCulloch and Walter Pitts in 1943, which is still the basis for most artificial neural networks. The model consists of a linear combiner followed by a hard limiter. The net input is applied to the hard limiter, which produces an output equal to +1 if its input is positive and - 1 if it is negative. [nn]

A mathematical function that defines a fuzzy set on the universe of discourse. Typical membership functions used in fuzzy expert systems are triangles and trapezoids. [fl]

see Degree of membership. [fl]

Knowledge about knowledge; knowledge about the use and control of domain knowledge in expert systems. [es]

A rule that represents metaknowledge. A metarule determines a strategy for the use of task-specific rules in the expert system. [es]

A procedure associated with an attribute of a frame. A method can determine the attribute’s value or execute a series of actions when the attribute’s value changes. Most frame-based expert systems use two types of methods: WHEN CHANGED and WHEN NEEDED. Method and demon are often used as synonyms. [es]

A positive constant less than unity included in the delta rule. The use of momentum accelerates learning in a multilayer perceptron and helps to prevent it from getting caught in a local minimum. [nn]

The most common topology of an ANN in which perceptrons are connected together to form layers. A multilayer perceptron has the input layer, at least one hidden layer and the output layer. The most popular method of training a multilayer perceptron is back-propagation. [nn]

The ability of an object or a frame to inherit information from multiple superclasses. [es]

A genetic operator that randomly changes the gene value in a chromosome. [ec]

A number between zero and one that indicates the probability of mutation occurring in a single gene. [ec]

A classic rule-based expert system developed in the 1970s for the diagnosis of infectious blood diseases. The system used certainty factors for managing uncertainties associated with knowledge in medical diagnosis. [es]

The process by which the most fit individuals have a better chance to mate and reproduce, and thereby to pass their genetic material on to the next generation. [ec]

A computational approach to modelling the human brain that relies on connecting a large number of simple processors to produce complex behaviour. Neural computing can be implemented on specialised hardware or with software, called artificial neural networks, that simulates the structure and functions of the human brain on a conventional computer. [nn]

A system of processing elements, called neurons, connected together to form a network. The fundamental and essential characteristic of a biological neural network is the ability to learn. Artificial neural networks also have this ability; they are not programmed, but learn from examples through repeated adjustments of their weights. [nn]

A cell that is capable of processing information. A typical neuron has many inputs (dendrites) and one output (axon). The human brain contains roughly 1012 neurons. Also, a basic processing element of an ANN that computes the weighted sum of the input signals and passes the result through its activation function to generate an output. [nn]

A decision point of a decision tree. [dm]

A random external disturbance that affects a transmitted signal. Noisy data contain errors associated with the way the data was collected, measured and interpreted. [dm]

A logical operator used for representing the negation of a statement. [es]

A concept, abstraction or thing that can be individually selected and manipulated, and that has some meaning for the problem at hand. All objects have identity and are clearly distinguishable. Michael Black, Audi 5000 Turbo, IBM Aptiva S35 are examples of objects. In object-oriented programming, an object is a self-contained entity that consists of both data and procedures to manipulate the data. [es]

A programming method that uses objects as a basis for analysis, design and implementation. [es]

An individual that was produced through reproduction. Also referred to as a Child. [ec]

A database used for the daily operation of an organisation. Data in operational databases is regularly updated. [dm]

A high-level programming language derived from LISP for developing rule-based expert systems. [es]

An iterative process of improving the solution to a problem with respect to a specified objective function.

A logical operator; when used in a production rule, it implies that if any of the antecedents joined with OR is true, then the rule consequent must also be true. [es]

A state in which an ANN has memorised all the training examples, but cannot generalise. Overfitting may occur if the number of hidden neurons is too big. The practical approach to preventing overfitting is to choose the smallest number of hidden neurons that yields good generalisation. Also referred to as Over-training. [nn]

see Overfitting. [nn]

The last layer of neurons in an ANN. The output layer produces the output pattern of the entire network. [nn]

A neuron in the output layer. [nn]

A computational technique that carries out multiple tasks simultaneously. The human brain is an example of a parallel information-processing system: it stores and processes information simultaneously throughout the whole biological neural network, rather than at specific locations. [nn]

An individual that produces one or more other individuals, known as offspring or child. [ec]

In a decision tree, a parent node is a node that splits its data between nodes at the next hierarchical level of the tree. The parent node contains a complete data set, while child nodes hold subsets of that set. [dm]

Identification of visual or audio patterns by computers. Pattern recognition involves converting patterns into digital signals and comparing them with patterns already stored in the memory. Artificial neural networks are successfully applied to pattern recognition, particularly in such areas as voice and character recognition, radar target identification and robotics. [nn]

The simplest form of a neural network, suggested by Frank Rosenblatt. The operation of the perceptron is based on the McCulloch and Pitts neuron model. It consists of a single neuron with adjustable synaptic weights and a hard limiter. The perceptron learns a task by making small adjustments in the weights to reduce the difference between the actual and desired outputs. The initial weights are randomly assigned and then updated to obtain an output consistent with the training examples. [nn]

A statistical evaluation of fitness. [ec]

A graph that shows the average performance of the entire population and the performance of the best individual in the population over the chosen number of generations. [ec]

Picture Element; a single point in a graphical image. Computer monitors display pictures by dividing the screen into thousands (or millions) of pixels arranged into rows and columns. The pixels are so close together that they appear as one image. [ke]

A group of individuals that breed together. [ec]

see Antecedent. [es]

A quantitative description of the likely occurrence of a particular event. Probability is expressed mathematically as a number with a range between zero (an absolute impossibility) to unity (an absolute certainty). [es]

A self-contained arbitrary piece of computer code. [es]

A term often used by cognitive psychologists to describe a rule. [es]

A statement expressed in the IF (antecedent) THEN (consequent) form. If the antecedent is true, then the consequent is also true. Also referred to as Rule. [es]

A high-level programming language developed at the University of Marseilles in the 1970s as a practical tool for programming in logic; a popular language for artificial intelligence.

An expert system for mineral exploration developed by the Stanford Research Institute in the late 1970s. To represent knowledge, PROSPECTOR used a combined structure that incorporated production rules and a semantic network. [es]

Query tool

Software that allows a user to create and direct specific questions to a database. A query tool provides the means for extracting the desired information from a database. [dm]

The process of drawing conclusions or inferences from observations, facts or assumptions. [es]

see Data record. [dm]

see Feedback network. [nn]

The process of creating offspring from parents. [ec]

see Root node. [dm]

The top-most node of a decision tree. The tree always starts from the root node and grows down by splitting the data at each level into new nodes. The root node contains the entire data set (all data records), and child nodes hold subsets of that set. Also referred to as Root. [dm]

A method of selecting a particular individual in the population to be a parent with a probability equal to its fitness divided by the total fitness of the population. [ec]

see Production rule. [es]

The knowledge base that contains a set of production rules. [es]

An expert system whose knowledge base contains a set of production rules. [es]

The second step in fuzzy inference; the process of applying the fuzzy inputs to the antecedents of fuzzy rules, and determining the truth value for the antecedent of each rule. If a given rule has multiple antecedents, the fuzzy operation of intersection or union is carried out to obtain a single number that represents the result of evaluating the antecedent. [fl]

A rule that expresses a heuristic. [es]

A method of correlating the consequent of a fuzzy rule with the truth value of the rule antecedent. It is based on adjusting the original membership function of the rule consequent by multiplying it by the truth value of the rule antecedent. Scaling helps to preserve the original shape of the fuzzy set. [fl]

The process of examining a set of possible solutions to a problem in order to find an acceptable solution. [es]

The set of all possible solutions to a given problem. [es]

see Unsupervised learning. [nn]

A method of knowledge representation by a graph made up of labelled nodes and arcs, where the nodes represent objects and the arcs describe relationships between these objects. [es]

A collection of elements (also called members).

The study of sets or classes of objects. The set is the basic unit in mathematics. Classical set theory does not acknowledge the fuzzy set, whose elements can belong to a number of sets to some degree. Classical set theory is bivalent: the element either does or does not belong to a particular set. That is, classical set theory gives each member of the set the value of 1, and all members that are not within the set a value of 0.

A bit string of ones, zeros and asterisks, where each asterisk can assume either value 1 or 0. For example, the schema stands for a set of four 4-bit strings with each string beginning with 1 and ending with 0. [ec]

A theorem that relates the expected number of instances of a given schema in the consequent generation with the fitness of this schema and the average fitness of chromosomes in the current generation. The theorem states that a schema with above-average fitness tends to occur more frequently in the next generation. [ec]

The process of choosing parents for reproduction based on their fitness. [ec]

A technique of determining how sensitive the output of a model is to a particular input. Sensitivity analysis is used for understanding relationships in opaque models, and can be applied to neural networks. Sensitivity analysis is performed by measuring the network output when each input is set (one at a time) to its minimum and then its maximum values. [ke]

see Expert system shell. [es]

An activation function that transforms the input, which can have any value between plus and minus infinity, into a reasonable value in the range between 0 and 1. Neurons with this function are used in a multilayer perceptron. [nn]

A hard limit activation function that produces an output equal to +1 if its input is positive and - 1 if it is negative. [nn]

see Fuzzy singleton. [fl]

A component of a frame in a frame-based system that describes a particular attribute of the frame. For example, the frame "computer" might have a slot for the attribute "model". [es]

The body of a biological neuron. [nn]

A hard limit activation function that produces an output equal to +1 if its input is positive and 0 if it is negative. [nn]

A type of learning that requires an external teacher, who presents a sequence of training examples to the ANN. Each example contains the input pattern and the desired output pattern to be generated by the network. The network determines its actual output and compares it with the desired output from the training example. If the output from the network differs from the desired output specified in the training example, the network weights are modified. The most popular method of supervised learning is back-propagation. [nn]

The law according to which only individuals with the highest fitness can survive to pass on their genes to the next generation. [ec]

A character or a string of characters that represents some object. [es]

Reasoning with symbols. [es]

A chemically mediated connection between two neurons in a biological neural network, so that the state of the one cell affects the state of the other. Synapses typically occur between an axon and a dendrite, though there are many other arrangements. See also Connection. [nn]

see Weight. [nn]

see Leaf. [dm]

A data set used for testing the ability of an ANN to generalise. The test data set is strictly independent of the training set, and contains examples that the network has not previously seen. Once training is complete, the network is validated with the test set. [nn]

A specific value that must be exceeded before the output of a neuron is generated. For example, in the McCulloch and Pitts neuron model, if the net input is less than the threshold, the neuron output is –1. But if the net input is greater than or equal to the threshold, the neuron becomes activated and its output attains a value +1. Also referred to as Threshold value. [nn]

see Threshold. [nn]

A structure of a neural network that refers to the number of layers in the neural network, the number of neurons in each layer, and connections between neurons. Also referred to as Architecture. [nn]

An artificial problem, such as a game. Also, an unrealistic adaptation of a complex problem. [es]

see Learning. [nn]

A data set used for training an ANN. [nn]

see Activation function. [nn]

In general, the terms truth value and membership value are used as synonyms. The truth value reflects the truth of a fuzzy statement. For example, the fuzzy proposition x is A (0.7) suggests that element x is a member of fuzzy set A to the degree 0.7. This number represents the truth of the proposition. [fl]

A test designed to determine whether a machine can pass a behaviour test for intelligence. Turing defined the intelligent behaviour of a computer as the ability to achieve human-level performance in cognitive tasks. During the test a human interrogates someone or something by questioning it via a neutral medium such as a remote terminal. The computer passes the test if the interrogator cannot distinguish the machine from a human.

In classical set theory, the union of two sets consists of every element that falls into either set. For example, the union of tall men and fat men contains all men who are either tall or fat. In fuzzy set theory, the union is the reverse of the intersection, that is, the union is the largest membership value of the element in either set. [fl]

The range of all possible values that are applicable to a given variable. [fl]

A type of learning that does not require an external teacher. During learning an ANN receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data into appropriate categories. Also referred to as Self-organised learning. [nn]

A person who uses a knowledge-based system when it is developed. For example, the user might be an analytical chemist determining the molecular structures, a junior doctor diagnosing an infectious blood disease, an exploration geologist trying to discover a new mineral deposit, or a power system operator seeking an advice in an emergency. Also referred to as End-user. [es]

A means of communication between a user and a machine. [es]

see Data visualisation. [dm]

The value associated with a connection between two neurons in an ANN. This value determines the strength of the connection and indicates how much of the output of one neuron is fed to the input of another. Also referred to as Synaptic weight. [nn]

A procedure attached to a slot of a frame in a frame-based expert system. The WHEN CHANGED method is executed when new information is placed in the slot. [es]

A procedure attached to a slot of a frame in a frame-based expert system. The WHEN needed method is executed when information is needed for the problem solving, but the slot value is unspecified. [es]