There are only two video games my wife has ever enjoyed: Mario Kart, in which she has gleefully brought up the rear for the entirety of our family life; and Crash Bandicoot, of which she was, at one stage, the greatest player in the world. She completed every molecule of every Crash game in the 90s. I swear I saw her get 105% on one of them, but this being the 90s, I have filed that memory under "things I may have hallucinated in an altered state", along with Gary McAllister missing that penalty at Wembley, and the band Menswear. I have never been a completionist like her. For me, platformers are the greatest video game genre that I absolutely hate – be it manic miners, plumbers, hedgehogs, Mega Man, Aladdin or Earthworm Jim.

I am too much of a control freak to let another player screw up my good work. But I really wanted to try It Takes Two because, first, it was in every single top games of 2021 list and, second, the game is about a couple on the verge of divorce who must find a way to work together. And a little over a year ago, my wife and I were in the same situation. In It Takes Two, the spouses become tiny dolls who must work their way through their suddenly gigantic house, solving puzzles to reunite with their weeping daughter. In real life, we did family therapy.

Learn more about what comes next. As game worlds grow more vast and complex, making sure they are playable and bug-free is becoming increasingly difficult for developers. And gaming companies are looking for new tools, including artificial intelligence, to help overcome the mounting challenge of testing their products. A new paper by a group of AI researchers at Electronic Arts shows that deep reinforcement learning agents can help test games and make sure they are balanced and solvable. "Adversarial Reinforcement Learning for Procedural Content Generation," the technique presented by the EA researchers, is a novel approach that addresses some of the shortcomings of previous AI methods for testing games.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. As game worlds grow more vast and complex, making sure they are playable and bug-free is becoming increasingly difficult for developers. And gaming companies are looking for new tools, including artificial intelligence, to help overcome the mounting challenge of testing their products. A new paper by a group of AI researchers at Electronic Arts shows that deep reinforcement learning agents can help test games and make sure they are balanced and solvable. "Adversarial Reinforcement Learning for Procedural Content Generation," the technique presented by the EA researchers, is a novel approach that addresses some of the shortcomings of previous AI methods for testing games.

Many business leaders are overlooking a way to grow their company and capture untapped value. Most of them understand the superior value of business models built around subscription-based software as a service (SaaS) and models built around marketplaces that join together many buyers and sellers. Few, however, understand the exponential growth and value that comes when both of those strategies are combined with data and machine learning models. As well, many leaders simply haven't considered adding these strategies and models to their own business to create platform economics and growth -- whether they're running a startup, a midsize company, or a legacy organization. But the opportunity to integrate these three strategic elements is becoming a critical business imperative.

We present an approach for procedural content generation (PCG), and improving generalization in reinforcement learning (RL) agents, by using adversarial deep RL. Training RL agents for generalization over novel environments is a notoriously difficult task. One popular approach is to procedurally generate different environments to increase the generalizability of the trained agents. Here we deploy an adversarial model with one PCG RL agent (called Generator), and one solving RL agent (called Solver). The benefit is mainly two-fold: Firstly, the Solver achieves better generalization through the generated challenges from the Generator. Secondly, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. The Generator receives a reward signal based on the performance of the Solver which encourages the environment design to be challenging but not impossible. To further drive diversity and control of the environment generation, we propose the use of auxiliary inputs for the Generator. Thus, we propose adversarial RL for procedural content generation (ARLPCG), an adversarial approach which procedurally generates previously unseen environments with an auxiliary input as a control variable. Herein we describe this concept in detail and compare it with previous methods showing improved generalization, as well as a new method to create novel environments.

In order to train DeepMind the company elected to show the artificial intelligence platform YouTube videos of games being played, rather than go through the painstaking process of playing against the platform game after game. The aim was to try to strengthen a weakness with artificial intelligence: one of exploration. Most platforms are weak in attempting to find new places to go, and this is a step towards thinking creatively. The gaming platform used to help improve artificial intelligence was classic Atari video games. The main game used was Montezuma's Revenge, which is a 1984 platform game for Atari 8-bit computers.

So you've bought a shiny new games console, or a ridiculously powerful PC, or the latest smartphone iteration, and now you want to play games on it. Well, if you've been doing the whole gaming thing for years, you'll know which review sites to go to, what developers and publishers produce the best stuff and what everyone is looking forward to playing. But if you're just starting out, it can all be a bit … overwhelming. Every year around 1,000 new titles are released on consoles and PC, and there are more than 300,000 games available on the Apple App Store. So how are you supposed to work out what to play?

In this paper we introduce a computational model based on the concept of symmetry to generate visually pleasing maps of platform games. We cast the problem of generating symmetrical maps as an optimization task and propose a heuristic search algorithm to solve it. A user study using a platform game shows the advantage of our method over other approaches in terms of visual aesthetics and enjoyment. Another user study shows that our method is able to generate maps as visually pleasing as maps created by professional designers.

If you have a box of beloved video games that you haven't played for years stashed beneath your bed, now could be the time to cash them in. Retro gaming titles from Ice Climber to Pokemon are fetching hundreds as nostalgia trumps common sense - and now there's a calculator that lets you guess how much your favourites could be worth. The rise in demand for old pixelated titles flies in the face of hi-tech advances in gaming, with PlayStation launching a VR headset for more realistic experiences soon. Click on the module below to guess the value of retro games. Mobile users who can't see the game can visit MrGamez's website Retro gaming titles from Ice Climber to Pokemon are fetching hundreds of pounds as nostalgia trumps common sense - and now there's a calculator that lets you guess how much your favourites could be worth. Favourite platform games, beat'em ups and fantasy installments have appreciated rapidly in recent years, with ice Climber, first sold in 1985 for Nintendo's NES, for a price of 29.99 ( 43) selling on one occasion for a whopping 1,818 ( 2,617) This was for a factory-sealed copy, but if you own one still in its box, it could fetch almost 90 ( 130).