Let's take a look at an off-topic issue in this article: gaming. Why is it even worth to talk about gaming? First of all, gaming is the first decision driven activity a human can do. Second, gaming is a huge economic factor of today's digital business. Third, everybody loves to play. So, let's take a look at challenges to the developers of computer games.
One big challenge to the game developers of modern computer games is to create opponents that are challenging the player - but never too far. Always matching a grade of difficulty that evokes a kind of thrill to the player without being hopeless to beat the computer player. But why is it a challenge? Well, it is something that affects the player directly - and a frustrated player will be a hard judge to the game developer or publisher. We could say: adequate opponents in games are a kind of game quality.
Computers can always easily defeat up to 98% of human players
For a computer, it is no problem to beat a human player today. In the 1990s and early 2000s, computer opponents run with a simple scripted artificial intelligence, persisting of a simple script language. All actions were scripted in an if-then-else construction with support of a couple of timers and way points. (I have seen those scripts as part of the former Genesis game engine and they were awful).
Today, computers could (I guess) defeat up to 98% of human players with today's new kinds of navigation and self-learning algorithms. But those opponents are way to strong to entertain the human player of the game, because they would always defeat him. Unfortunately there is no way to tell the computer, to be only half as good, because failing is caused by errors which can be the consequence of a lack of experience of a human. But a computer can not simulate missing experience when he knows the whole game world or all possible tactics or how to use his actions most effective. Of course, the developers could tell the computer to use always 50% of effectiveness or 70%. But the player could be bored very fast of this. The challenge turned from creating stronger virtual opponents in the late 1990s to creating weaker virtual opponents - or more adequate opponents that are always just (for example) 4% weaker than the player? This is one of the challenges of the gaming industry.
It starts with simple games
The discussed problem is not just a challenge in highly complex games. Actually it concerns also the most simple kind of game. For example, let's take a look at Pong. Pong, one of the first computer games, was a simple Ping Pong simulation for two players. The challenge for both players was to hit the ball and avoid points for the opponent (the other human player).
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| Screenshot of a Pong-like game |
Currently, lots of attempts are taken to teach computers to play games - and to win them. The most famous one happened in 1997, when IBM's super computer "Deep Blue" defeated the world champion in chess, Garri Kasparov, in a chess tournament.
Since 1997, a whole couple of games have been learned to play by computers. For example, Google's artificial intelligence learned to play Atari games in 2015. Recently, an AI called "Alpha Star" defeated the world champion in Star Craft II, one of the most complex computer games.
Learning to play
It looks like the topic of the day in artificial intelligence is "learning to play". Not without a reason. As we could see, playing is very complex in decision-making. If an AI can play, it also can start handling more complex problems - maybe air surveillance or rail system traffic?
This facts are interesting enough to learn a little bit more about AI and playing computer games.
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