A clever little Nim like game. There’s a loop of patches, and you can spend buttons and time to buy any of the next three available patches (which moves the marker to the new empty slot), and you place the patch onto your grid. As you advance in time you get income (buttons on your quilt).
The player who has used less time goes next. First person to finish a 7×7 grid (out of 9×9) gets 7 bonus points, and when both players have run out of time you add buttons, subtract for unfinished portions of the quilt cost 2 points per.
Simple, elegant. Yes, also dry, but it’s a fast 2 player filler. Could burn your brain, if you want.
Rating — Indifferent, but a good sort. As an abstract I’ll never love it, but it’s fast and clever.
There is currently a bridgewinners discussion on “When will computers beat human bridge experts?“. This is (unsurprisingly) triggered by the recent advances in Go playing computers, based on the deep learning system. The news from Google — taking time out of their military robotics schemes to focus on less Skynet-y ventures — was an interesting demonstration. My only expertise in this (apart from the fact that I’m not exactly a stranger to military robotics programs, but also medical robotics!) is that I’ve followed computer opponents in classic games somewhat.
There are three salient points to the system — the training method, the use of monte carlo systems in evaluation, and the hybrid engine. For now, lets just consider a simplified bridge AI. It plays standard american, and expects its opponents to do the same. Teaching a program to handle multiple bidding systems is one of scale and scope, and not that different (in practice).
Training — The Go program was trained with 30 million expert positions, then played against itself to bootstrap. This method could be used with bridge, assuming a large enough corpus of expert deals exists. However, there are some issues.
Every go (and chess) program starts from the same board position, a fact that isn’t true of Bridge. To counter balance that the search space for an individual deal is much much smaller. Still, it’s not clear that 30 million deals is enough. Presumably you could use some non-expert deals for bidding (take random BBO hands and if enough people bid them the same way, that’s probably good enough). Top level deals can be entered, especially those with auctions duplicated at two tables.
Card play could use a similar method — for a hand and auction, if the opening lead is standard, you could assume (absent further training) that it is right. A clever AI programmer could have a program running on BBO playing hands, and then comparing it’s results (already scored, no less!) against others. Your scoring system may want to account for weird results (getting to good slams that fail on hideous breaks, etc), but that’s pretty simple.
So, there may be a problem getting enough expert deals, but there should be enough to get a large corpus of good deals (particularly if the engine weights others and then uses better players as a benchmark).
Randomness — Some people on the BW thread are saying that randomness will stop an AI.
The news out of Google is ahead of schedule, but it didn’t surprise me as much as Crazy Stone (the precursor to Alpha Go). Crazy Stone’s innovation was that if it couldn’t decide between two moves (because they were strategic, not tactical, or if the search depth got too great), it would simply play a few hundred random games from each position, and pick the move that scores better. Adding randomness to the evaluation function (of a non-random game!) greatly improved the structure, so much so that I believe I commented on it at the time. (Sadly, that was before the move, so I don’t have a tagged post. See my posts tagged go for some tangential comments.
Randomizing bridge hands would present different challenges, but the idea of just saying, “I don’t know, let’s just try each lead a few hundred times against random hands (that match with what we expect” is obvious, as well as using randomness (to decide whether to continue or shift suits). Because bridge doesn’t have Go’s massive search depth, you could also drop each hand into a single dummy solver for each position, or have it play randomly only until breaks are none (so plays randomly but not with a known position).
The thing about random play is that it’s fast. So you’ve won the opening lead, what to play? Whip up 100 random deals (not hard since you can see two hands plus a few other cards, plus all your bidding inferences) and try them out.
Hybridization — The trick is that you only resort to randomness if your trained algorithm isn’t confident of its training. This happens quite a bit in Go. (Go is amazingly frustrating in that expert or even master level players will be unable to communicate why a play is correct. I remember a lecture at the Pittsburgh Go Association and the lecturer, an amatuer 3 dan or so, was reviewing a game between two pros. And someone asked “Why did so-and-so play that move on that spot. Isn’t one space to the right better?”
Neither move had a tactical flaw, and the lecturer stumbled, then called out to a late arrival (a graduate student from Japan and — I believe — soon to turn Pro after getting his degree). The arrival went up to the big magnetic board, stared, said “Ah! It’s because of” and then laid out 10 moves for each side. Then reset, shifted the stone, and laid out ten different moves for each side then walked the few people who could understand the differences through it.
The point of my story? Go is hard. Go is hard enough so that the professional players routinely make moves that amateur experts cannot reasonably understand. Go experts can look much farther ahead than bridge players (and computers) — yet random simulation coupled with deep learning can handle it.
The Go playing program might very well have learned to play the move on the correct spot, and not one-to-the-right, in our example. How did it learn this? Because the experts did it. It gained a feel for what to do in those situations. But even assuming that it hadn’t learned, and was sitting in the back of the room (like a 20 year old me) and couldn’t see a difference between the two. It might still grope its way to the correct move using a Monte Carlo simulation on both moves. (This is assuming that it’s near term tactical engine couldn’t find both sequences and judge one obviously better).
Right now bridge computers have many advantages, and can play perfectly once enough is known about the hand. You’d never use a random engine at that point. This hybridized strategy would be for your master solver’s club type things where experts disagree.
And, if you are deciding between those two things, you are (by definition) an expert.
So, I stand of the opinion that Bridge hasn’t been solved because nobody has thought to attack it. Or perhaps there is not a large enough body of expert deals that can be conveniently fed into a computer. A clever programmer (which I am not) could probably have a system learn just by having it log onto BBO, assuming that it could learn which players to trust and which to not (and which ones to use as bidding examples). 30 Million deals, each played 4 times by experts may not be enough, but it’s probably in the ballpark.
Why hasn’t this been done? Probably nobody cares. Go is (by far) the sexiest game right now because it’s search space is unfathomably deep. Go players routinely scoff at the simplicity (by comparison) of chess. In terms of search space (for a single hand) bridge doesn’t compare. If Google put its money behind it, I think a Bridge computer would do well in a match against a top team. Also, there were prizes offered for Go programs that could play at a high enough level, which spurred on development over the last 20 years.
Or, your semi-quarterly-ish update on my media consumption.
I binged Better Call Saul this week. I had high expectations. They were surpassed. What can I say, I’m Vince Gilligan’s huckleberry. I may even pony up the cash to watch S2 now (especially since Archer is delayed a few months).
I was looking for a new show and I’m slowly getting into Parks and Recreation. It’ll do, it’s growing on me. After burning through seven+ seasons of Supernatural and then stopping, I’ve started watching again (slower, but I’ve finished S8).
Last night I watched the cult non-classic Tokyo Tribe. Imagine a Warriors Musical but with Hip-Hop/Rap for most of the lyrics. Bad Kung Fu featuring a cannibalistic fat Elvis, a dwarf butler, a blond body-builder in a banana-hammock, a growly beat-boxer schoolgirl, phones shaped like bedazzled pistols, a baseball-bat-wielding goon sporting a disco-ball coated samurai helmet, a scrappy kid straight out of the Feng Shui rulebook and an ending featuring a Jackie-Chan style ending where all the misguided youths join forces to defeat the truly evil. And all in Japanese, so go back and tack on that adjective to everything (“Japanese fat Elvis”, “Japanese blond body builder…”).
And rapping (in Japanese). I actually liked most of the lyrics, which sport a fair chunk of Japanglish.
Enjoyable but terrible, made interestingly terrible because the director considers himself an auter (I judge), sort of a Japanese Tarantino. He’s trying to do interesting things in a pulpy setting and not always failing. Lots of nudity, bad kung-fu, but also several long (and obvious) no-edit pieces, a gonzo attitude, a few laughs — some at, some with — and insanity. By the same guy as Why Don’t you Play in Hell? and that can be your determination if you should check this out. (On Netflix).
Based on an earlier thread, I read Aurora. Interesting. I quickly clued in that there was some weird linguistic styling and deduced why, but that didn’t make it less annoying. Still, finished it.
Still haven’t watched Spectre or Star Wars. I may go watch Hail Caesar tomorrow (I’ve seen all the Coen movies except The Lady Killers, and that’s in my queue now).
I watched F is for Family, and I am willing to admit it. It was not great, but it was not bad, and only 3 hours.
The Very Murray Christmas was exactly what it was.
Comedy — Anthony Jeselnik’s special (Thoughts and Prayers) is amazing, in the sense that after five minutes you know his schtick (Andrew Dice Clay but rude and horrible) and you know the rough shape of each joke before it hits, yet its still damn funny. John Mulaney works much cleaner, also funny. Eugene Mirman’s special (Vegan on his way to the Complain Store) is just him telling stories, truly weird.
Documentaries — I watched the Journey doc about the new frontman for Journey (Don’t Stop Believin’: Everyman’s Journey) and a documentary on Rush (Beyond the Lighted Stage). Liked both. Also Magic Camp about a summer camp for teenage magicians. That was interesting in many ways, both as a statement on young obsession, technical skills, and some get-off-my-lawn observations about gender.
As always, the queue is weak and suggestions are welcome.
I don’t read facebook, but I’ve been alerted to this piece of information:
Late this afternoon David [Hartwell] had a massive brain bleed from which he is not expected to recover.
In case the name isn’t familiar to you, that’s not surprising. David G. Hartwell is a famous editor, not a writer. Gaming and SF have always had a relationship, I first went to SF cons (specifically, Balticon) out of curiosity, but I gravitated to the gaming connection. As such, it was possible that I would have eventually run into (or been introduced to) David Hartwell, but not likely.
In fact I already knew him before I went to my first convention. He taught a six week writing class (“Writing Science Fiction and Fantasy”) I took over a summer when I was seventeen.
Ms. Tao also attended that same institution, and her writing instructor was Ben Bova. At the time, I thought it strange that one section had a famous writer and the other had … a not so famous editor. But honestly I suspect I got much more out of that class than if we’d been in the other sections. For one thing, David was incredibly supportive. I don’t think anyone from that class went on to be a published writer (but even as I am bad with names now, I was practically antagonistic towards learning them in my youth, so who knows?) and gave me excellent feedback and hours of practical advice on how to get published. I mean, I know he did, even if I don’t remember the details.
All I remember were the personal stories. Those were great. Funny and engaging, Our section had a lot more laughter and joy, by all accounts.
The only real lesson that stuck with me? Being an editor could be a lot of fun, if you were willing to accept the limitations of the profession. The impression I had was that David enjoyed the hell out of his life, mingling with the interesting people that make up our little clique, finding new talent, and making just enough money to survive. In hindsight, I just think that David would have had fun, no matter what he was doing.
It’s a small circle, SF. One that I’m not really in.
But I was lucky enough to meet him and learn from him, nearly thirty years ago.
Update — Neil Gaiman posted to twitter that David has died. RIP.
So I picked up the R4TG Xeno expansion and banished Alien Artifacts back to the orb from which it came. Unlike some people, (such as the distinguised gentleman E___ B___) two base sets of Race is enough for me, thanks. AA was fine, but I didn’t care for the Orb game and in any case it’s easier to disassemble the the full Arc #1, so away it goes).
So far I’ve only played one (non-Invasion) game, but it seems fine. Lots of cards, so the built in “Mix explores with your hand” will go a long way. But I’ll be pushing this at game night, so more games soon. This year marks (for me, I think) the 10th anniversary of Race (since I played it at conventions); I’m glad to see a new expansion.
One problem is that I stuff the game in the (smaller) expansion box (to make sure which version I grabbed, since the first arc is in the main box) but that means that I don’t have the cheat sheets, which don’t fold and are too big for the box. But I suspect I’ll have non-new players for the most part.
I didn’t pick up the new Eclipse expansion, because I’m in no rush and also: I worry about adding yet more stuff to it (even thought it’s modular) will push it over the brink. Yes, it’s a varietal expansion, but Eclipse games pretty much “throw everything in.”
I’ll pick it up eventually, though. I’m still hoping Eclipse is one of the Fifty by Fifty.
Oh no love! you’re not aloneYou’re watching yourself but you’re too unfairYou got your head all tangled up but if i could onlyMake you careOh no love! you’re not aloneNo matter what or who you’ve beenNo matter when or where you’ve seenAll the knives seem to lacerate your brainI’ve had my share, I’ll help you with the painYou’re not alone!