Group items tagged

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.

3. JSON Class hinting ¶ There are only simple data types defined in JSON. To overcome this problem in a JSON compatible way a special property for objects is introduced. {"__jsonclass__":["constructor", [param1,...]], "prop1": ...} The object is then instantiated using the constructor, passing in the parameters. Once constructed the properties (prop1, ...) will be applied.

To give the serverside peer a chance to send messages to the client side peer, the client side peer may reopen communication by sending an empty HTTP POST.

--> {"method": "postMessage", "params": ["Hello all!"], "id": 99} <-- {"result": 1, "error": null, "id": 99} <-- {"method": "handleMessage", "params": ["user1", "we were just talking"], "id": null} <-- {"method": "handleMessage", "params": ["user3", "sorry, gotta go now, ttyl"], "id": null} --> {"method": "postMessage", "params": ["I have a question:"], "id": 101} <-- {"method": "userLeft", "params": ["user3"], "id": null} <-- {"result": 1, "error": null, "id": 101}

Theory of mind is the ability to attribute mental states—beliefs, intents, desires, pretending, knowledge, etc.—to oneself and others and to understand that others have beliefs, desires and intentions that are different from one's own.[1]

One of the most important milestones in theory of mind development is gaining the ability to attribute false belief: that is, to recognize that others can have beliefs about the world that are wrong. To do this, it is suggested, one must understand how knowledge is formed, that people’s beliefs are based on their knowledge, that mental states can differ from reality, and that people’s behavior can be predicted by their mental states. Numerous versions of the false-belief task have been developed, based on the initial task done by Wimmer and Perner (1983).

In the most common version of the false-belief task (often called the ‘Sally-Anne’ task), children are told or shown a story involving two characters. For example, in one version, the child is shown two dolls, Sally and Anne, playing with a marble. The dolls put away the marble in a box, and then Sally leaves. Anne takes the marble out and plays with it again, and after she is done, puts it away in a different box. Sally returns and the child is then asked where Sally will look for the marble. The child passes the task if she answers that Sally will look in the first box where she put the marble; the child fails the task if she answers that Sally will look in the second box, where the child knows the marble is hidden, even though Sally cannot know, since she did not see it hidden there. In order to pass the task, the child must be able to understand that another’s mental representation of the situation is different from their own, and the child must be able to predict behavior based on that understanding. The results of research using false-belief tasks have been fairly consistent: most normally-developing children are unable to pass the tasks until around the age of three or four.

In computer science, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is relatively simple to implement, making it a popular first choice. Although more advanced algorithms may give better results, in some situations hill climbing works just as well. Hill climbing can be used to solve problems that have many solutions, some of which are better than others. It starts with a random (potentially poor) solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal, but it is not guaranteed that hill climbing will ever come close to the optimal solution. For example, hill climbing can be applied to the traveling salesman problem. It is easy to find a solution that visits all the cities but will be very poor compared to the optimal solution. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. Eventually, a much better route is obtained. Hill climbing is used widely in artificial intelligence, for reaching a goal state from a starting node. Choice of next node and starting node can be varied to give a list of related algorithms.

COUNT(*) returns the number of items in a group. This includes NULL values and duplicates. COUNT(ALL expression) evaluates expression for each row in a group and returns the number of nonnull values. COUNT(DISTINCT expression) evaluates expression for each row in a group and returns the number of unique, nonnull values.

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.