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Islam TeCNo

Deep Blue (chess computer) - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/...Deep_Blue_(chess_computer)

shared by Islam TeCNo on 25 Jun 09 - Cached
  • Deep Blue was a chess-playing computer developed by IBM. On May 11, 1997, the machine won a six-game match by two wins to one with three draws against world champion Garry Kasparov.[1] Kasparov accused IBM of cheating and demanded a rematch, but IBM declined and dismantled Deep Blue.[2] Kasparov had beaten a previous version of Deep Blue in 1996
    • Abdelrahman Ogail
      Abdelrahman Ogail on 25 Jun 09
       
      When AI beats humanity!
  • Deep Blue was then heavily upgraded (unofficially nicknamed "Deeper Blue")[11] and played Kasparov again in May 1997, winning the six-game rematch 3½–2½, ending on May 11, finally ending in game six, and becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls.
  • The system derived its playing strength mainly out of brute force computing power.
    • Islam TeCNo
      Islam TeCNo on 25 Jun 09
       
      Dah eli bysamoh brute force men no3 el 7aywan :D
fili el sayed

GameDev.net -- How do I make games? A Path to Game Development - 0 views

www.gamedev.net/...makegames

Game Development Games

shared by fili el sayed on 14 Jun 09 - Cached
  • Ahmed Mansour on 14 Jun 09
     
    Nice one fili :D thanks man :)
Islam TeCNo

OpenGL - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/OpenGL

shared by Islam TeCNo on 10 Jun 09 - Cached
  • OpenGL (Open Graphics Library) is a standard specification defining a cross-language, cross-platform API for writing applications that produce 2D and 3D computer graphics. The interface consists of over 250 different function calls which can be used to draw complex three-dimensional scenes from simple primitives. OpenGL was developed by Silicon Graphics Inc. (SGI) in 1992[1] and is widely used in CAD, virtual reality, scientific visualization, information visualization, and flight simulation. It is also used in video games, where it competes with Direct3D on Microsoft Windows platforms (see Direct3D vs. OpenGL). OpenGL is managed by the non-profit technology consortium, the Khronos Group.
    • Mohamed Abd El Monem
      Mohamed Abd El Monem on 10 Jun 09
       
      just some info about OGL :)
    • Islam TeCNo
      Islam TeCNo on 10 Jun 09
       
      fe so2al bytra7 nafso !! ...ezaii el developers by3mlo API byshta3'l ma3 ay lo3'a !! ...ya3ni men el python OpenGL men el C++ OpenGL men el C# OpenGL!!
  • Mark Segal and Kurt Akeley authored the original OpenGL specification
    • Islam TeCNo
      Islam TeCNo on 10 Jun 09
       
      2 Names to remember :D
    • Islam TeCNo
      Islam TeCNo on 10 Jun 09
       
      LOL @ Book Names
  • ...2 more annotations...
  • (which actually has a white cover)
    • Islam TeCNo
      Islam TeCNo on 10 Jun 09
       
      Realy LOL :D :D
  • The OpenGL standard allows individual vendors to provide additional functionality through extensions as new technology is created. Extensions may introduce new functions and new constants, and may relax or remove restrictions on existing OpenGL functions. Each vendor has an alphabetic abbreviation that is used in naming their new functions and constants. For example, NVIDIA's abbreviation (NV) is used in defining their proprietary function glCombinerParameterfvNV() and their constant GL_NORMAL_MAP_NV.
Janos Haits

ARC Browser - 2 views

arcbrowser.com/info

browsers iron chromium browser media tools privacy Computer development technology service online

shared by Janos Haits on 08 Sep 22 - No Cached
  • Janos Haits on 08 Sep 22
     
    "ARC Browser is a rom collection browser and emulator frontend that maintains a database of all your games, presented in a user friendly way, and let's you play them using your favorite emulators."
Abdelrahman Ogail

Simulated annealing - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...Simulated_annealing

AI AI-In-Games Adaptation

shared by Abdelrahman Ogail on 18 Aug 09 - Cached
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • ...1 more annotation...
  • Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Abdelrahman Ogail on 18 Aug 09
     
    Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration - provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. By analogy with this physical process, each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima-which are the bane of greedier methods. The method was independently described by S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi in 1983 [1], and by V. Černý in 1985 [2]. The method is an adaptation of the Metropolis-Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by N. Metropolis et al. in 1953 [3].
  • Abdelrahman Ogail on 18 Aug 09
     
    A natural AI approach
Abdelrahman Ogail

Artificial life - Wikipedia, the free encyclopedia - 2 views

en.wikipedia.org/...Artificial_life

Artificial-Intelligence alife AI-In-Games

shared by Abdelrahman Ogail on 07 Aug 09 - Cached
  • Artificial life (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry.[1] There are three main kinds of alife[2], named for their approaches: soft[3], from software; hard[4], from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate biological phenomena.[5] The term "artificial life" is often used to specifically refer to soft alife
  • The modeling philosophy of alife strongly differs from traditional modeling, by studying not only “life-as-we-know-it”, but also “life-as-it-might-be” [7].
Islam TeCNo

Neuroplasticity - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/...Neuroplasticity

shared by Islam TeCNo on 25 Jun 09 - Cached
  • Neuroplasticity (also referred to as brain plasticity, cortical plasticity or cortical re-mapping) is the changing of neurons and the organization of their networks and so their function by experience. This idea was first proposed in 1892 by Santiago Ramón y Cajal the proposer of the neuron doctrine though the idea was largely neglected for the next fifty years.[1] The first person to use the term neural plasticity appears to have been the Polish neuroscientist Jerzy Konorski.[2]
    • Abdelrahman Ogail
      Abdelrahman Ogail on 25 Jun 09
       
      This is why when a person thinks more he/she be more smarter!
    • Islam TeCNo
      Islam TeCNo on 25 Jun 09
       
      3azeem .....bas deh mesh 7agat related l CS ya Zi3'az
    • Abdelrahman Ogail
      Abdelrahman Ogail on 25 Jun 09
       
      How said that? It's related to Artificial Neural Networks that require understanding of actual Human Neurons. BTW, plasticity principle is used in Games AI where the Controlled-AI determines if it forgot what happened or still remember it and seeks to revenge
    • Islam TeCNo
      Islam TeCNo on 25 Jun 09
       
      oooooooooh ........ 3'reaaaaaat
Islam TeCNo

Model-view-controller - Wikipedia, the free encyclopedia - 0 views

en.wikipedia.org/...2%80%93view%E2%80%93controller

shared by Islam TeCNo on 09 Jun 09 - Cached
  • 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.
    • Abdelrahman Ogail
      Abdelrahman Ogail on 09 Jun 09
       
      MVC one of the important patterns used at any software. Especially in Web Development, Database Systems and sure in Game Development
    • Islam TeCNo
      Islam TeCNo on 09 Jun 09
       
      please ya zikas 7ot more comments l eni mesh fahem awi ...ana eli fahmo eni afsl el GUI 3an el core code
  • 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.
    • Islam TeCNo
      Islam TeCNo on 09 Jun 09
       
      i think this is like PHP or ASP page .... you just See HTML (view) that is Generated by PHP/ASP Code (controller) that gather data from Database (content)
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