User:Block based programmer/Antiobjects - Revision history

Deepfriedokra: Deepfriedokra moved page User:Dragentsheets/Antiobjects to User:Block based programmer/Antiobjects: Automatically moved page while renaming the user "Dragentsheets" to "Block based programmer"

2021-04-25T16:38:39Z

Deepfriedokra moved page User:Dragentsheets/Antiobjects to User:Block based programmer/Antiobjects: Automatically moved page while renaming the user "Dragentsheets" to "Block based programmer"

← Previous revision	Revision as of 16:38, 25 April 2021
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Dawn Bard: tagged as userpage per WP:FAKEARTICLE

2013-03-27T14:30:05Z

tagged as userpage per WP:FAKEARTICLE

← Previous revision		Revision as of 14:30, 27 March 2013
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			{{userpage\|noindex=yes}}
	The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.		The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.

Jclemens: tweaking for userspace

2011-07-28T06:35:31Z

tweaking for userspace

← Previous revision		Revision as of 06:35, 28 July 2011
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	<!-- Please do not remove or change this AfD message until the issue is settled -->
	{{Article for deletion/dated\|page=Antiobjects\|timestamp=20110214122526\|year=2011\|month=February\|day=14\|substed=yes}}
	<!-- For administrator use only: {{Old AfD multi\|page=Antiobjects\|date=14 February 2011\|result='''keep'''}} -->
	<!-- End of AfD message, feel free to edit beyond this point -->
	The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.		The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.

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	== References ==		== References ==

			{{reflist}}
	<references/>

	[[Category:Object-oriented programming]]		<!-- [[Category:Object-oriented programming]] -->

Jclemens: moved Antiobjects to User:Dragentsheets/Antiobjects: Userifying per request at DRV

2011-07-28T06:34:00Z

moved Antiobjects to User:Dragentsheets/Antiobjects: Userifying per request at DRV

← Previous revision	Revision as of 06:34, 28 July 2011
(No difference)

Balabiot: AfD: Nominated for deletion; see Wikipedia:Articles for deletion/Antiobjects

2011-02-14T12:25:27Z

AfD: Nominated for deletion; see Wikipedia:Articles for deletion/Antiobjects

← Previous revision		Revision as of 12:25, 14 February 2011
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			<!-- Please do not remove or change this AfD message until the issue is settled -->
			{{Article for deletion/dated\|page=Antiobjects\|timestamp=20110214122526\|year=2011\|month=February\|day=14\|substed=yes}}
			<!-- For administrator use only: {{Old AfD multi\|page=Antiobjects\|date=14 February 2011\|result='''keep'''}} -->
			<!-- End of AfD message, feel free to edit beyond this point -->
	The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.		The notion of '''antiobjects''' is a computational metaphor useful to conceptualize and solve hard problems by swapping computational foreground and background. Similar to [[optical illusions]] based on potential confusion of [[Figure-ground (perception)\|background versus foreground perceptions]], antiobjects are the inverse of what we perceive to be the computational objects. If we implement, as part of a [[Pac-Man]] game, a ghost we are tempted to think of the necessary behavior associated with the ghost object; if we simulate the behavior of an air bubble in a water glass we are tempted to think of how the bubble object should behave; if we build a soccer simulation we are tempted to think of how the soccer player objects should interact with the ball and other player objects. Antiobjects turn things on their head. In the case of Pacman we put the main computation into the maze; to simulate the behavior of an air bubble we put the main computation into the water; to create a collaborative soccer game we put the main computation into the soccer field.

152.81.11.204: /* Examples */

2010-07-09T11:25:58Z

Examples

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	Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example		Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example
	<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation		<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation
	Antoine Spicher Nazim Fatès, and Olivier Simonin, [~~Revised Selected Papers of ICAART 2009~~ http://www.springer.com/computer/ai/book/978-3-642-11818-0], CCIS 67 , 2010, p. 422-429 </ref>		Antoine Spicher Nazim Fatès, and Olivier Simonin, [http://www.springer.com/computer/ai/book/978-3-642-11818-0 Revised Selected Papers of ICAART 2009], CCIS 67 , 2010, p. 422-429 </ref>

	== Reaction Diffusion ==		== Reaction Diffusion ==

152.81.11.204: /* Examples */

2010-07-09T11:24:32Z

Examples

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	<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation		<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation
	Antoine Spicher Nazim Fatès, and Olivier Simonin, [Revised Selected Papers of ICAART 2009 http://www.springer.com/computer/ai/book/978-3-642-11818-0], CCIS 67 , 2010, p. 422-429 </ref>		Antoine Spicher Nazim Fatès, and Olivier Simonin, [Revised Selected Papers of ICAART 2009 http://www.springer.com/computer/ai/book/978-3-642-11818-0], CCIS 67 , 2010, p. 422-429 </ref>

	Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example
	<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation
	Antoine Spicher Nazim Fatès, and Olivier Simonin, Revised Selected Papers of ICAART 2009 , CCIS 67 , 2010, p. 422-429 </ref>

	== Reaction Diffusion ==		== Reaction Diffusion ==

152.81.11.204: /* Pac-Man example */

2010-07-09T11:24:10Z

Pac-Man example

← Previous revision		Revision as of 11:24, 9 July 2010
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	Putting computation into antiobjects, e.g., the maze, the water, and the soccer field, can substantially simplify hard problems in [[Artificial intelligence\|Artificial Intelligence]] and simulations. Moreover, the mapping of computation from a small number of objects to a much larger number of typically homogenous antiobjects can by employed to [[Parallelization\|parallelize]] computation in ways that it can be executed on parallel architectures such as [[Graphics processing unit\|GPUs]] and [[Multi-core (computing)\| multi-core CPUs]] with very little overhead.		Putting computation into antiobjects, e.g., the maze, the water, and the soccer field, can substantially simplify hard problems in [[Artificial intelligence\|Artificial Intelligence]] and simulations. Moreover, the mapping of computation from a small number of objects to a much larger number of typically homogenous antiobjects can by employed to [[Parallelization\|parallelize]] computation in ways that it can be executed on parallel architectures such as [[Graphics processing unit\|GPUs]] and [[Multi-core (computing)\| multi-core CPUs]] with very little overhead.

	== ~~Pac-Man example~~ ==		== Examples ==

	Usually the technique involves creating many small "antiobject" objects, for the [[Pac-Man]] example, one antiobject per background tile is created. Each of these antiobjects or agents has an identical and simple algorithm which it runs at every turn of the game. Instead of making Ghosts smart enough to solve "shortest path" problems around the maze, a notion of "Pac-Man scent" is created instead and each tile is responsible for saying how much Pac-Man scent is on its tile. Using [[Alan Turing]]'s [[reaction-diffusion equation]] this turns out to be a simple distributed solution which runs on each instance of tile. The tile asks other objects or antiobjects located on top of it for their Pac-Man scent value, then it asks its 4 nearest tiles for their scent. Lastly, given these inputs, it solves the [[differential equation]], which simulates the spread of Pac-Man scent. Note that the walls of the maze, and Ghosts themselves, all block the diffusion of Pac-Man scent. These local differences affect the outcome of each tile's amount of scent and always provide a gradient that leads to Pac-Man. Besides the reaction-diffusion equation, no difficult algorithms, such as topology problems, are needed, yet a correct and accurate solution emerges. The Ghosts are given a simple hill climbing algorithm to walk towards higher quantities of Pac-Man scent.		Usually the technique involves creating many small "antiobject" objects, for the [[Pac-Man]] example, one antiobject per background tile is created. Each of these antiobjects or agents has an identical and simple algorithm which it runs at every turn of the game. Instead of making Ghosts smart enough to solve "shortest path" problems around the maze, a notion of "Pac-Man scent" is created instead and each tile is responsible for saying how much Pac-Man scent is on its tile. Using [[Alan Turing]]'s [[reaction-diffusion equation]] this turns out to be a simple distributed solution which runs on each instance of tile. The tile asks other objects or antiobjects located on top of it for their Pac-Man scent value, then it asks its 4 nearest tiles for their scent. Lastly, given these inputs, it solves the [[differential equation]], which simulates the spread of Pac-Man scent. Note that the walls of the maze, and Ghosts themselves, all block the diffusion of Pac-Man scent. These local differences affect the outcome of each tile's amount of scent and always provide a gradient that leads to Pac-Man. Besides the reaction-diffusion equation, no difficult algorithms, such as topology problems, are needed, yet a correct and accurate solution emerges. The Ghosts are given a simple hill climbing algorithm to walk towards higher quantities of Pac-Man scent.

	This pattern of making each tile an agent and allowing them to look at the state of its neighbors is also how [[Conway's Game of Life]] works, as well as all [[Cellular automaton\|cellular automata]]. Similarly, notice the simple rules and naturally parallel nature of computation, also like the game of life.		This pattern of making each tile an agent and allowing them to look at the state of its neighbors is also how [[Conway's Game of Life]] works, as well as all [[Cellular automaton\|cellular automata]]. Similarly, notice the simple rules and naturally parallel nature of computation, also like the game of life.
			Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example
			<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation
			Antoine Spicher Nazim Fatès, and Olivier Simonin, [Revised Selected Papers of ICAART 2009 http://www.springer.com/computer/ai/book/978-3-642-11818-0], CCIS 67 , 2010, p. 422-429 </ref>

	Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example		Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example

152.81.11.204: /* Pac-Man example */

2010-07-09T11:22:11Z

Pac-Man example

← Previous revision		Revision as of 11:22, 9 July 2010
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	This pattern of making each tile an agent and allowing them to look at the state of its neighbors is also how [[Conway's Game of Life]] works, as well as all [[Cellular automaton\|cellular automata]]. Similarly, notice the simple rules and naturally parallel nature of computation, also like the game of life.		This pattern of making each tile an agent and allowing them to look at the state of its neighbors is also how [[Conway's Game of Life]] works, as well as all [[Cellular automaton\|cellular automata]]. Similarly, notice the simple rules and naturally parallel nature of computation, also like the game of life.

			Spicher, Fatès and Simonin proposed a general scheme to turn an agent-based model into a cellular automaton. The authors studied the case of Diffusion-Limited Aggregation as a starting example
			<ref> Translating Discrete Multi-Agents Models into Cellular Automata, Application to Diffusion-Limited Aggregation
			Antoine Spicher Nazim Fatès, and Olivier Simonin, Revised Selected Papers of ICAART 2009 , CCIS 67 , 2010, p. 422-429 </ref>

	== Reaction Diffusion ==		== Reaction Diffusion ==

Enkyo2: /* Overview */

2010-06-09T18:03:45Z

Overview

← Previous revision		Revision as of 18:03, 9 June 2010
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	From the abstract of “Collaborative Diffusion: Programming Antiobjects”<ref>Repenning, A., [http://www.cs.colorado.edu/~ralex/papers/PDF/OOPSLA06antiobjects.pdf Collaborative Diffusion: Programming Antiobjects.] [http://www.oopsla.org/2006/submission/onward/collaborative_diffusion:_programming_antiobjects.html in OOPSLA 2006], ACM SIGPLAN International Conference on Object-Oriented Programming Systems, Languages, and Applications. Portland, Oregon: ACM Press, 2006.</ref>:		From the abstract of “Collaborative Diffusion: Programming Antiobjects”<ref>Repenning, A., [http://www.cs.colorado.edu/~ralex/papers/PDF/OOPSLA06antiobjects.pdf Collaborative Diffusion: Programming Antiobjects.] [http://www.oopsla.org/2006/submission/onward/collaborative_diffusion:_programming_antiobjects.html in OOPSLA 2006], ACM SIGPLAN International Conference on Object-Oriented Programming Systems, Languages, and Applications. Portland, Oregon: ACM Press, 2006.</ref>:

	<blockquote>The metaphor of objects can go too far by making us try to create objects that are too much inspired by the real world. This is a serious problem, as a resulting system may be significantly more complex than it would have to be, or worse, will not work at all. We postulate the notion of an antiobject as a kind of object that appears to essentially do the opposite of what we generally think the object should be doing. As a [[Thought experiment\|Gedankenexperiment]] antiobjects allow us to literally think outside the proverbial box or, in this case outside the object.</blockquote>		<blockquote>The metaphor of objects can go too far by making us try to create objects that are too much inspired by the real world. This is a serious problem, as a resulting system may be significantly more complex than it would have to be, or worse, will not work at all. We postulate the notion of an antiobject as a kind of object that appears to essentially do the opposite of what we generally think the object should be doing. As a [[Thought experiment\|Gedankenexperiment]] antiobjects allow us to literally [[Thinking outside the box\|think outside the proverbial box]] or, in this case outside the object.</blockquote>

	Putting computation into antiobjects, e.g., the maze, the water, and the soccer field, can substantially simplify hard problems in [[Artificial intelligence\|Artificial Intelligence]] and simulations. Moreover, the mapping of computation from a small number of objects to a much larger number of typically homogenous antiobjects can by employed to [[Parallelization\|parallelize]] computation in ways that it can be executed on parallel architectures such as [[Graphics processing unit\|GPUs]] and [[Multi-core (computing)\| multi-core CPUs]] with very little overhead.		Putting computation into antiobjects, e.g., the maze, the water, and the soccer field, can substantially simplify hard problems in [[Artificial intelligence\|Artificial Intelligence]] and simulations. Moreover, the mapping of computation from a small number of objects to a much larger number of typically homogenous antiobjects can by employed to [[Parallelization\|parallelize]] computation in ways that it can be executed on parallel architectures such as [[Graphics processing unit\|GPUs]] and [[Multi-core (computing)\| multi-core CPUs]] with very little overhead.