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Simulation

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The New SimCity Will Turn You Into An Urban Planning Nut. SimCity, a city-building simulation series that was first released in 1989, has always been a virtual sandbox for aspiring urban planners, with a seemingly endless array of options--you could lay down roads; zone houses, industrial complexes, and commercial real estate; put up nuclear power plants; adjust taxation; and more.

The New SimCity Will Turn You Into An Urban Planning Nut

In the end, you could destroy your whole empire with a UFO or a well-placed asteroid strike. The newest version of SimCity, set to be released in February 2013, retains most of the game’s previous elements (including its addictive quality) while bringing a whole new level of complexity to the tilt-shift inspired world. You might not even notice how Maxis is subtly teaching you about the pros and cons of renewable energy, preserving natural resources, and cooperating with neighboring cities. I recently visited Maxis--the division of Electronic Arts behind SimCity--to learn more (and play a demo of the game, of course). Here’s what I found out. Automatic Simulation Queueing in R. I spend much of my time writing R code for simulations to compare the supervised classification methods that I have developed with similar classifiers from the literature.

Automatic Simulation Queueing in R

A large challenge is to determine which datasets (whether artificial/simulated or real) are interesting comparisons. Even if we restricted ourselves to multivariate Gaussian data, there are a large number of covariance matrix configurations that we could use to simulate the data. In other words, there are too many possibilities to consider all of them. However, it is often desirable to consider as many as possible. Parallel processing certainly has reduced the runtime for simulations. I have been searching for ways to automate a lot of what I do, so I can spend less time on the mundane portions of simulation and focus on classification improvement. To actually queue the simulation, we make a call to queue.sim(): Let’s look at an example to see what is actually happening. A note about the shell file created.

The A.I. Revolution Is On. Today’s A.I. bears little resemblance to its initial conception.

The A.I. Revolution Is On

The field’s trailblazers believed success lay in mimicking the logic-based reasoning that human brains were thought to use. Photo: Dwight Eschliman; Illustration: Zee Rogér Diapers.com warehouses are a bit of a jumble. Boxes of pacifiers sit above crates of onesies, which rest next to cartons of baby food. In a seeming abdication of logic, similar items are placed across the room from one another. But the warehouses aren’t meant to be understood by humans; they were built for bots.

The computers are in control. The Kiva bots may not seem very smart. This explosion is the ironic payoff of the seemingly fruitless decades-long quest to emulate human intelligence. All aboard the algorithm. Model trains are easy to keep track of. What they got was the Princeton Locomotive and Shop Management System, or Plasma, which used an algorithmic strategy to analyze Norfolk Southern’s operations.

—Jon Stokes. But we must learn to adapt. Agent-based model. An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole.

Agent-based model

It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs),[1] and individuals within IBMs may be simpler than fully autonomous agents within ABMs. Agent-based models are a kind of microscale model [3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence from the lower (micro) level of systems to a higher (macro) level. History[edit] Early developments[edit] Theory[edit]