I’ve always thought of top-flight AI players as a sort of death knell for strategy games. That way lies solved systems, where memorization is king. A blog post about AlphaGo, however, suggests a different use for them: exploring new tactical avenues .

In sum, the article points out that AlphaGo (a very skilled AI player of the classic game Go) sometimes does things human players have long written off as ill-advised. Never embarrassed by the risk of making a rookie mistake, and able to think about the entire board with each move, AlphaGo explores strategies that the Go world has dismissed or even actively rejected. That allows it to find not just new sequences of plays, but to reveal innovative approaches to the game.

RoboRosewater showed us that computers can help us brainstorm. AlphaGo has a new lesson on offer: if a game is deep enough, AI players can direct us toward avenues of complexity that humans have yet to explore. Computer assists can be valuable at both ends of a game’s lifecycle.

Thanks all for your patience while I was away!

Privateer Press formally opened their publicly-accessible playtesting today. I’m eager to see how it works out; running good playtests is demanding, so both the company and the community have their work cut out for them. Here’s hoping for good things; public beta tests could be more valuable than I think they have been for many outfits that have tried them, and I would love to see Privateer Press taking a lead in this area.

In the meantime, I think it’s interesting to see how Privateer Press describes playtesting. Many designers considering public betas have to figure out how to explain what they need to a competitive player community. The linked document is one of what I hope will someday be many examples.

It’s a given that large-scale digital games like Hearthstone can collect a lot of data. More interesting, I think, is how the designers interpret what they gather. What kinds of information do they use? How do they interpret the patterns they see?

Those questions make this post by Ben Brode, Hearthstone’s Game Director, worthy of study. He highlights particular statistics, and notes what they mean to the Hearthstone design team. It’s a great window into how professional designers running a major product grapple with both design problems, and the overwhelming amount of raw data that’s meant to help them address game issues.

NYU’s Tisch School of the Arts runs a program for high school students interested in game design. Applications were due in November, but . . .

Details here!

Computers do all sorts of great things. Unfortunately, taking advantage of a computer’s unique capabilities–pathfinding, generating content mathematically, effortless line-of-sight evaluations, etc.–can involve getting through substantial technical barriers. If you’re being kept from your design goals by one of those walls, I strongly recommend taking a look at Red Blob Games.

Red Blob Games does two things that are very, very useful in providing code help. First, it has lots of examples. Second, and perhaps more importantly, it explains clearly how things work. The examples are all the more useful as a result, and it’s (relatively) easy to envision the changes needed to build the implementation you need for your particular project.

Some ideas hinge on a technical thing that must work. If your technical thing is on Red Blob Games’ pages, that’s a big head start. Give them a look.

This year’s talks are starting to appear on the GDC Vault website. I haven’t had a chance to see which ones are up, but I felt it was worth noting the availability of the new videos if for no other reason than as a reminder that a lot of great older content is there, as well. Take a look!