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An Abstract Agent Programming Language or Artificial Autonomous Agents Programming Language or 3APL (pronounced triple-A-P-L) is an experimental tool and programming language for the development, implementation and testing of multiple cognitive agents using the Belief-Desire-Intention (BDI) approach. The newest incarnation of 3APL is 2APL (A Practical Agent Programming Language).

MathML is an XML standard by W3C and, embedded in XHTML,

The Belief-Desire-Intention (BDI) software model (usually referred to simply, but ambiguously, as BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.

n mathematics, a ternary relation or triadic relation is a finitary relation in which the number of places in the relation is three. Ternary relations may also be referred to as 3-adic, 3-ary, 3-dimensional, or 3-place. Just as a binary relation is formally defined as a set of pairs, i.e. a subset of the Cartesian product A × B of some sets A and B, so a ternary relation is a set of triples, forming a subset of the Cartesian product A × B × C of three sets A, B and C.

Model–view–controller (MVC) is an architectural pattern used in software engineering. Successful use of the pattern isolates business logic from user interface considerations, resulting in an application where it is easier to modify either the visual appearance of the application or the underlying business rules without affecting the other. In MVC, the model represents the information (the data) of the application; the view corresponds to elements of the user interface such as text, checkbox items, and so forth; and the controller manages the communication of data and the business rules used to manipulate the data to and from the model.

MVC is often seen in web applications, where the view is the actual HTML or XHTML page, and the controller is the code that gathers dynamic data and generates the content within the HTML or XHTML. Finally, the model is represented by the actual content, which is often stored in a database or in XML nodes, and the business rules that transform that content based on user actions.

Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.

Flocking behavior is the behavior exhibited when a group of birds, called a flock, are foraging or in flight. There are parallels with the shoaling behavior of fish, or the swarming behavior of insects. Computer simulations and mathematical models which have been developed to emulate the flocking behaviors of birds can generally be applied also to the "flocking" behavior of other species. As a result, the term "flocking" is sometimes applied, in computer science, to species other than birds. This article is about the modelling of flocking behavior. From the perceptive of the mathematical modeller, "flocking" is the collective motion of a large number of self-propelled entities and is a collective animal behavior exhibited by many living beings such as birds, fish, bacteria, and insects.[1] It is considered an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Flocking behavior was first simulated on a computer in 1986 by Craig Reynolds with his simulation program, Boids. This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The result is akin to a flock of birds, a school of fish, or a swarm of insects.

The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.

The Clockwork Universe Theory is a theory, established by Isaac Newton, as to the origins of the universe. A "clockwork universe" can be thought of as being a clock wound up by God and ticking along, as a perfect machine, with its gears governed by the laws of physics. What sets this theory apart from others is the idea that God's only contribution to the universe was to set everything in motion, and from there the laws of science took hold and have governed every sequence of events since that time. This idea was very popular during the Enlightenment, when scientists realized that Newton's laws of motion, including the law of universal gravitation, could explain the behavior of the solar system. A notable exclusion from this theory though is free will, since all things have already been set in motion and are just parts of a predictable machine. Newton feared that this notion of "everything is predetermined" would lead to atheism. This theory was undermined by the second law of thermodynamics ( the total entropy of any isolated thermodynamic system tends to increase over time, approaching a maximum value) and quantum physics with its unpredictable random behavior.

A system in a steady state has numerous properties that are unchanging in time. The concept of steady state has relevance in many fields, in particular thermodynamics. Steady state is a more general situation than dynamic equilibrium. If a system is in steady state, then the recently observed behavior of the system will continue into the future. In stochastic systems, the probabilities that various different states will be repeated will remain constant.

The Belief-Desire-Intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories.

The Belief-Desire-Intention (BDI) software model (usually referred to simply, but ambiguously, as BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.

Mutation testing (or Mutation analysis) is a method of software testing, which involves modifying program's source code in small ways.[1] These, so-called mutations, are based on well-defined mutation operators that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero). The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.

For example, consider the following C++ code fragment: if (a && b) c = 1; else c = 0; The condition mutation operator would replace '&&' with '||' and produce the following mutant: if (a || b) c = 1; else c = 0;

Many mutation operators can produce equivalent mutants. For example, consider the following code fragment: int index=0; while (...) { . . .; index++; if (index==10) break; } Boolean relation mutation operator will replace "==" with ">=" and produce the following mutant: int index=0; while (...) { . . .; index++; if (index>=10) break; }

Stochastic optimization (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices, etc.), or in both [1]. The concept contrasts with the deterministic optimization methods, where the values of the objective function are assumed to be exact, and the computation is completely determined by the values sampled so far.