Then Apple Arcade dropped during the iOS 13 beta, letting me check out what was on offer. Immediately, the selection of games was overwhelming. When iOS 13 proper landed, it was the kind of launch line-up other systems would kill for. There were 71 titles in all, from tiny indie delicacies that would find it hard to survive as standalone titles, through to new releases from giants like Capcom. Since that first moment, I’ve been working my way through every game, to play every one at least a little, and therefore get an idea as to who Apple Arcade is aimed at, and whether it’s worth subscribing to.
Craig Grannell writes about games on iOS and on Mac, so he’s the guy who really understands this stuff. I’m not a gamer, so the whole Apple Arcade thing doesn’t interest me, even though I tried.
I’ve always been intrigued by Othello, but I never really grokked the game. It’s deceptively simple, but the strategy needed to win the came is far more complex than one would think. I’ve tried a few Othello apps, but was always frustrated by losing and never went very far.
Anders Kierulf, developer of SmartGo and other apps, has just released SmartOthello. This beautifully designed app has five levels, from novice to kick-your-ass, and also helps you play a better game. It’s “Blunder Guard” feature (sounds like something from a swashbuckler story) warns you when you make a dumb move, and explains why. It has Game Center support, so you can play against friends and others, and includes a collection of games by the world’s best players so you can see how Othello is really played.
I’ve known Anders Kierulf for a while, since I have long played go, but I never knew that he was the 1992 US Othello champion, so this game is made by someone who really knows how it works. (He also wrote a PhD thesis on go and Othello… You may find it interesting to listen to this interview with Anders on The Committed podcast, where we discussed go and artificial intelligence.)
And just yesterday, I learned that longtime Mac journalist Ted Landau is also an avid Othello player; he was US champion in 1984. On his website he offers a free download of a book he wrote about Othello strategy. I’ve grabbed the book, and will have a read to see if I can learn more about how to play this game.
SmartOthello is $3 on the App Store. Check it out now and find a new favorite game.
Go is a board game, originally from Asia, that is played on a board with 19 x 19 lines. Two players take turns placing stones (one player gets white, the other black) on the intersections of the lines. The goal is to create a territory; the space delimited by your stones. At the end of the game, you count up the points (intersections) in your territory, and add any stones you have captured (you can capture stones by surrounding them, and removing them from the board). The person with the highest score wins.
(This means positions where stones are allowed to play according to the rules. And I’ve added line breaks so the number doesn’t stretch out off the side of the page.)
It’s very hard to write an AI for go. While chess is relatively easy to beat, because there are only 64 squares, and the game is much simpler, go has long been hard to solve.
Google’s AlphaGo project has made a brilliant breakthrough recently, defeating Fan Hui, the European champion 5 games to 0. The Google page explains how complicated it was to develop their AI:
But as simple as the rules are, Go is a game of profound complexity. The search space in Go is vast — more than a googol times larger than chess (a number greater than there are atoms in the universe!). As a result, traditional “brute force” AI methods — which construct a search tree over all possible sequences of moves — don’t have a chance in Go. To date, computers have played Go only as well as amateurs. Experts predicted it would be at least another 10 years until a computer could beat one of the world’s elite group of Go professionals.
Go requires a different form of AI from chess. Again, here’s how Google explains it:
AlphaGo’s search algorithm is much more human-like than previous approaches. For example, when Deep Blue played chess, it searched by brute force over thousands of times more positions than AlphaGo. Instead, AlphaGo looks ahead by playing out the remainder of the game in its imagination, many times over – a technique known as Monte-Carlo tree search. But unlike previous Monte-Carlo programs, AlphaGo uses deep neural networks to guide its search. During each simulated game, the policy network suggests intelligent moves to play, while the value network astutely evaluates the position that is reached. Finally, AlphaGo chooses the move that is most successful in simulation.
Go AIs have used the Monte Carlo approach for a while now, but never on this scale.
There is a bit of hubris in Google’s presentation of this event:
We are thrilled to have mastered Go and thus achieved one of the grand challenges of AI.
It’s fair to say that they’ve done very well, but “mastered;” not quite. AlphaGo plans to take on Lee Sedol, the leading go player in the world, in March, to see if that claim is true.
Anders Kierulf, developer of go software, has written an article about AlphaGo vs Fan Hui, and also about the coming match against Lee Sedol. Anders’ conclusions are interesting:
Fan Hui made a number of mistakes that Lee Sedol is unlikely to make.
While AlphaGo played very well, it did make some mistakes in those five games. Also, Fan Hui did win two unofficial games against AlphaGo (sadly unpublished).
AlphaGo’s reading (looking ahead many moves to determine whether a plan will work or not) is very strong.
AlphaGo sometimes mimics the play of professional players and follows standard patterns that may not be optimal in that specific situation. Professional players are more creative and will vary their play more based on subtle differences in other parts of the board.
AlphaGo may not have a nuanced enough understanding of the value of sente (having the initiative).
AlphaGo doesn’t show deep understanding of why a move is played, or the far-reaching effects of a move.
And he points out what we don’t know about AlphaGo:
Ko was only played once; AlphaGo did well, but we don’t know how it will do in a complex, protracted ko fight. We don’t know how it will do when the fighting gets more complex. We don’t know how it will do when the board is more fluid and multiple local positions are left unresolved.
You can download a PDF from the British Go Journal with the game records and some commentary on the games, or see Anders Kierulf’s article for links to other commentaries. This program is clearly very strong, and will undoubtedly get better, but can it truly reproduce the creativity and intuition of top human players? Or is that a few more years away?
Do you want to learn how to play go? Check out Anders Kierulf’s SmartGo apps, which let you play games, save and analyze game records, and read go books on iOS devices. Read this Macworld article I wrote about a year ago about those and some other apps. I really hope that Google makes a limited version of this AI available so go players can try it out. Naturally, such a version wouldn’t be as strong – part of the strength of an AI is its ability to use a large number of processors – but it would be great to have a go app to play against that is good enough to help people learn to play better.
A friend plays Two Dots a lot, and is irked by the fact that, after you have lost five lives, you have to either wait a while, or pay to get more. I really dislike this sort of gameplay, where you get hit for in-app purchases all the time. So I don’t mind telling you how to get free lives.
Since these games use a timer, all you need to do is change the time on your device. On an iPhone or iPad, go to Settings > General > Date & Time, then toggle off Set Automatically.
Move the date wheel ahead one day, then go back to the game. In most cases, you’ll be able to get free lives, but sometimes, I’ve found that Two Dots needs to be nudged a second time.
If you have a different device, find the Date & Time setting and make the same change.
Some games actually check the time to make sure you haven’t tweaked; they confirm the time on a server. So you need to put your phone in airplane mode, change the time, start playing, then turn off airplane mode.
I’d rather pay for a game than have this in-app purchase system. These games work by addicting you, frustrating you that you can’t finish a level, and offering you the opportunity to “buy a refill.” It’s a lame way to make money, and they do make lots of money. So here’s how you can play more without paying.
I like to play go. It’s a board game, originally from Asia, that is played on a board with 19 x 19 lines. You take turns placing stones (one player gets white, the other black) on the intersections of the lines. The goal is to create a territory; space delimited by your stones. At the end of the game, you count up the points (intersections) in your territory, and add any stones you have captured (you can capture stones by surrounding them). The person with the highest score wins.
That was a very, very succinct description of the game of go (or baduk, in Korean, or weiqi in Chinese). While the rules are simple, it does get more complicated than that. The game is played professionally, mostly in Japan, Korea and China, and has developed a long tradition of strategy and tactics. You could say that the depth of study is similar to that of chess, though the game’s logic is totally different: while you can kill stones, the goal is to make territory, unlike in chess where the only goal is to kill pieces.
Another difference between go and chess is the ability of computer programs to successfully play the game. While software can beat chess grandmasters, no go software comes anywhere near the level of professionals (though people are trying hard). This is, in part, due to the number of possible moves at any time (at the first move, there are 361 points where one can play, though the first few moves are usually only played on one of a couple of dozen points), but also to the number of moves in a game (games range from 200 to 300 moves).
I’ve been playing go for many years, casually at first, then, in the early days of the Internet I started playing on the now defunct NNGS (No-Name Go Server), a server that connected people around the world. I now play on KGS, where my screen name is Dogen. Unfortunately, I live in an area devoid of go players or clubs, but with KGS I can play at any time of the day or night, and I get to play people from many different countries and styles.
So, for years I had wanted to get a nice go set. I had a cheap folding board with glass stones; fine to play the game, but not aesthetically pleasing. I finally made the investment in a nice set, ordered from Kuroki Goishi Ten in Japan, a manufacturer of go stones, boards and bowls. As you can see in the picture above, those are the three elements of a go set: a board, black and white stones, and bowls to hold the stones.
The board is made from hyuga kaya, a type of tree found in Japan, and is made of four pieces of wood glued together. A board’s price depends, in part, on the number of pieces of wood it uses: the more pieces, the cheaper. The most expensive boards are made of a single piece of wood, and this is very expensive because of the size of the piece needed and the impeccable quality it must have. Next come boards with two pieces of wood, with a joint in the middle. Then come four-piece boards, and then five- to seven-piece boards. The wood used for my board is beautiful; kaya has a yellowish tint to it, and the grain on the top is very straight. In addition, the four pieces of wood are joined at points just under lines, so you cannot even see the joints.
The stones are quite special. The black stones are made of slate, and are really “stones”; they are black, not the usual gray slate people are familiar with, and have a matte finish. The white stones are made from clamshells and have grain on one side. They are smooth and shiny, and contrast well with the black stones. There are three different grades of clamshell stones; from least to most expensive: flower, moon and snow. I chose moon, because the grain is more attractive (on snow stones, the grain is less obvious). They also come in different thicknesses; mine are 8.4 mm thick, which I find quite nice to hold. Many players prefer thicker, heavier stones.
Finally come the bowls. Perhaps the least esthetic part of a set, mine are made of cherry blossom wood, and have a beautiful glowing finish and very prominent grain.
What strikes me most about this set is the overall esthetic quality of the different elements and how they all fit together. The craftsmanship of this material is magnificent, showing that one can own hand-made objects even in our mechanical age at affordable prices.
But I said I don’t have anyone to play with. It’s a shame, but the only use I’ll have (for now) for this set is to play games on the board as I play them on a go server, or to play out pro games to study. I very much enjoy doing the latter, as it is a form of meditation; when one is absorbed in a game, the outside world fades away and one’s concentration peaks. For now, I’m a slightly-better-than-average player, but I’m getting better, through study and practice. Wish me luck!