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Abdelrahman Ogail

Clockwork universe theory - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...Clockwork_universe_theory

ALife Artificial-Intelligence

shared by Abdelrahman Ogail on 07 Aug 09 - Cached
  • 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.
    • Abdelrahman Ogail
      Abdelrahman Ogail on 07 Aug 09
       
      "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" <-- ???
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
Ahmed Mansour

CRC Cards Tutorial - 0 views

users.csc.calpoly.edu/...crc_b

CRC oop

shared by Ahmed Mansour on 04 Jul 09 - Cached
  • Object Oriented Analysis and Design using CRC Cards
  • A CRC cards is an index card that is use to represent the responsibilities of classes and the interaction between the classes. CRC cards are an informal approach to object oriented modeling. The cards are created through scenarios, based on the system requirements, that model the behavior of the system. The name CRC comes from Class, Responsibilities, and Collaborators which the creators found to be the essential dimensions of object oriented modeling.
    • Ahmed Mansour
      Ahmed Mansour on 04 Jul 09
       
      One of the most popular methods for identifying and categorizing classes is to use class-responsibility-collaboration cards (CRC). Each CRC card represents a single class's data attributes, responsibilities, and collaborations. source : Thought Process Book..
    • Ahmed Mansour
      Ahmed Mansour on 04 Jul 09
       
      You need to create three sections on each card: - The name of the class - The responsibilities of the class - The collaborations of the class
  • Why uses CRC cards?
  • ...4 more annotations...
  • They are portable... No computers are required so they can be used anywhere. Even away from the office. The allow the participants to experience first hand how the system will work. No computer tool can replace the interaction that happens by physically picking up the cards and playing the roll of that object... The are a useful tool for teaching people the object-oriented paradigm. They can be used as a methodology them selves or as a front end to a more formal methodology such as Booch, Wirfs-Brock, Jacobson, etc.
  • Tutorial
    • Ahmed Mansour
      Ahmed Mansour on 04 Jul 09
       
      here we can found simple tutorial for illustration...
  • A CRC cards is an index card that is use to represent the responsibilities of classes and the interaction between the classes. CRC cards are an informal approach to object oriented modeling. The cards are created through scenarios, based on the system requirements, that model the behavior of the system. The name CRC comes from Class, Responsibilities, and Collaborators which the creators found to be the essential dimensions of object oriented modeling.
  • Why uses CRC cards? They are portable... No computers are required so they can be used anywhere. Even away from the office. The allow the participants to experience first hand how the system will work. No computer tool can replace the interaction that happens by physically picking up the cards and playing the roll of that object... The are a useful tool for teaching people the object-oriented paradigm. They can be used as a methodology them selves or as a front end to a more formal methodology such as Booch, Wirfs-Brock, Jacobson, etc.
  • Ahmed Mansour on 04 Jul 09
     
    This is great tutorial for CRC cards ( sort of object oriented modeling approach) \ni think it was useful for me ... hoping to be useful for all of us : ) ..
Janos Haits

Quantum Internet - QuTech - 0 views

qutech.nl/...quantum-internet

physics quantum internet quantumcomputing Technology development computing resources knowledge web science

shared by Janos Haits on 08 Feb 22 - No Cached
  • Janos Haits on 08 Feb 22
     
    "Search Division of Quanum Internet"
Janos Haits

Quantiki | Quantum Information Portal and Wiki - 1 views

www.quantiki.org

ai technology science artificial intelligence physics wiki Information research Computer tools online service web

shared by Janos Haits on 20 Feb 23 - Cached
  • Janos Haits on 20 Feb 23
     
    "The world's leading portal for everyone involved in quantum information science. No matter if you are a researcher, a student or an enthusiast of quantum theory, this is the place you are going to find useful and enjoyable! While here on Quantiki you can: browse our content, including fascinating and educative articles, then create your own account and log in to gain more editorial possibilities."
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