Reward systemThe reward system (the mesocorticolimbic circuit) is a group of neural structures responsible for incentive salience (i.e., "wanting"; desire or craving for a reward and motivation), associative learning (primarily positive reinforcement and classical conditioning), and positively-valenced emotions, particularly ones involving pleasure as a core component (e.g., joy, euphoria and ecstasy). Reward is the attractive and motivational property of a stimulus that induces appetitive behavior, also known as approach behavior, and consummatory behavior.
Software design patternIn software engineering, a software design pattern is a general, reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. Rather, it is a description or template for how to solve a problem that can be used in many different situations. Design patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system.
Computer data storageComputer data storage is a technology consisting of computer components and recording media that are used to retain digital data. It is a core function and fundamental component of computers. The central processing unit (CPU) of a computer is what manipulates data by performing computations. In practice, almost all computers use a storage hierarchy, which puts fast but expensive and small storage options close to the CPU and slower but less expensive and larger options further away.
Action selectionAction selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior. One problem for understanding action selection is determining the level of abstraction used for specifying an "act".
Taylor seriesIn mathematics, the Taylor series or Taylor expansion of a function is an infinite sum of terms that are expressed in terms of the function's derivatives at a single point. For most common functions, the function and the sum of its Taylor series are equal near this point. Taylor series are named after Brook Taylor, who introduced them in 1715. A Taylor series is also called a Maclaurin series when 0 is the point where the derivatives are considered, after Colin Maclaurin, who made extensive use of this special case of Taylor series in the mid-18th century.
Decision modelA decision model in decision theory is the starting point for a decision method within a formal (axiomatic) system. Decision models contain at least one action axiom. An action is in the form "IF is true, THEN do ". An action axiom tests a condition (antecedent) and, if the condition has been met, then (consequent) it suggests (mandates) an action: from knowledge to action. A decision model may also be a network of connected decisions, information and knowledge that represents a decision-making approach that can be used repeatedly (such as one developed using the Decision Model and Notation standard).
QuantityQuantity or amount is a property that can exist as a multitude or magnitude, which illustrate discontinuity and continuity. Quantities can be compared in terms of "more", "less", or "equal", or by assigning a numerical value multiple of a unit of measurement. Mass, time, distance, heat, and angle are among the familiar examples of quantitative properties. Quantity is among the basic classes of things along with quality, substance, change, and relation.
Hidden Markov modelA hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it — with unobservable ("hidden") states. As part of the definition, HMM requires that there be an observable process whose outcomes are "influenced" by the outcomes of in a known way.
Independent component analysisIn signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.
UnderstandingUnderstanding is a cognitive process related to an abstract or physical object, such as a person, situation, or message whereby one is able to use concepts to model that object. Understanding is a relation between the knower and an object of understanding. Understanding implies abilities and dispositions with respect to an object of knowledge that are sufficient to support intelligent behavior. Understanding is often, though not always, related to learning concepts, and sometimes also the theory or theories associated with those concepts.
