Gaming with Science

Gaming with Science Podcast

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Gaming with Science is a podcast that looks at science through the lens of tabletop board games. If you ever wondered how natural selection shows up in Evolution, whether Cytosis reflects actual cell metabolism, or what the socioeconomics of Monopoly are, this is the place for you. (And if not, we hope you’ll give us a try anyway.) So grab a drink, pull up a chair, and let’s have fun playing dice with the universe!

5h ago

S3E05.5 - Minecraft (Bonus - Teaching Computers to Game)

#Minecraft #MinecraftBasalt #NeuralNetworks #ArtificialIntelligence #AI #TeachingComputersToGame #BoardGames #Science #SciComm Summary In our final minisode about teaching computers to game, we leave the tabletop behind and move on to Minecraft and even the real world. We're also back with Dr. Prithvi Akella, who helps us understand how Minecraft and other digital games provide more open-ended platforms to work on AI models, along with what an "AI agent" actually is (no, not a spy--well, _probably_ not a spy) and how they're used to run tasks in both the game and real worlds. We also talk about what large language models actually are, how they and vision-based models work, and happens when you let a thousand AIs loose on their own Minecraft server. So get ready to punch some wood in our final minisode of this series for Gaming with Science. Timestamps 00:00 Introductions 01:04 What is Minecraft? 03:23 Teaching AIs to play Minecraft 07:18 AI agents and LLMs 10:45 Letting AI loose in Minecraft 17:49 No more games for AI? 20:54 So what about us humans? Links Minecraft official site (Mojang) Altera setting AI agents loose in Minecraft (Video 1 , Video 2 ) (YouTube) MineRL Challenge (also MineRL BASALT) (ReadTheDocs.io) Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Jason Wallace 0:04 Jason, hello, and welcome to the Gaming with Science podcast, where we talk about the science behind some of your favorite games. In today's minisode about teaching computers to game, we'll be talking about Minecraft and the next frontier of machine learning. All right, everyone. Welcome back to Game with Science. This is Jason. This is Brian, and we are once again joined by our special guest, Dr. Prithvi Akela, who is here to help us understand machine learning and AI and the world of Minecraft today. Prithvi, can you do a quick introduction for the people who may have forgotten since last week? Prithvi 0:36 Hello, everyone, nice to speak to you all again. My name is Prithvi. I finished my PhD from Caltech about three years ago, where my emphasis was on validation of learning enabled systems with a goal of trying to make sure that these systems function more reliably and safely in general practice. Jason Wallace 0:51 All right, and in our final episode of this four part mini series on teaching computers to games, we have left the realm of board games and gone into computer games, so we are going to be talking about Minecraft today. Brian 1:02 Finally, a real game. Jason Wallace 1:04 I don't play Minecraft. Minecraft is a game with very pixely art. I see my daughters playing, and they seem to have lots of fun building farms and villages and making artwork and rugs and stuff in it, and it looks like digital Legos, and that's about all I know about it. So I'm going to pass it to Brian to explain to us what is Minecraft. Brian 1:23 Sure, I think digital Legos is actually a great analogy for Minecraft. So, as a player, you'll spawn into a big wilderness expanse all made out of blocks. It's been around for over 15 years at this point. It was released in 2011 so that's very long legs for a video game. It's had routine updates throughout the time that have sort of kept people interested, add new things, new features. Basically, you can collect the blocks in the world, all these natural resources, craft them into other things, build structures, build castles, stuff like that. You are supposed to eat food at night time, monsters will spawn, so you have to like make yourself armor and weapons to protect yourself. It's generally a sandbox game, in the sense that, like, the player usually is the one who decides what they want to do. It's sort of very open-ended, which is probably why a lot of kids enjoy it's very creative. Again, it's like imagine you had an infinite box of Legos without having to worry about things like gravity, and also you get to fight monsters at the same time. One of the key things about Minecraft, though, is that each world is procedurally randomly generated, so based on a seed and a bunch of noise maps, as you keep walking, the world is technically unbounded. Obviously, you can't actually do infinite, because your computer will explode at a certain point, but as you keep walking in a direction, there will always be a next horizon, a next hill, a next forest, a next ocean, all based on this seed procedural number map, there is technically an end to the game, where, like, you can get to a state where the game will play its end credits. You have to do some pretty esoteric, sort of obscure things to get to the end of the game, which involves, like, building multiple interdimensional portals, collecting resources from two specific monsters, finding a rare structure underground again, using more rare resources, and then fighting the dragon boss at the end of the game. I think maybe that's one of the things that makes it interesting as a challenge is every time you spawn into a Minecraft world, unless you have artificially plugged in the exact same seed, that world will be unique and different. You can always beat it, but things will not be in the same place, the resources will not be in the same place. The way to beat the game will not be in the same place. Jason Wallace 3:23 And I think this is part of the appeal of why games like this were the next frontier after Go was mastered, is because Go and other board games have very finite states. It's all bounded in this board. You know exactly what the pieces are you can work with, and you have very clear goals. In chess, it's to capture the opponent's king. In Go, it's to capture the most territory on the board. Minecraft, as I understand it, does not have specific goals like that. There are many, many things you can do, but not really anything you have to. There's no single goal for the game, and so that makes it, to my mind, one step closer to reality, where we have this massive unbounded sphere we all live on, and we are trying to do all sorts of things, and so it seems like Minecraft is sort of like a stepping stone to being able to get these AI agents to work in the real world, is to first train them in a simpler world that has known rules, but not quite as many of them, and if someone messes up in Minecraft, no one dies, Jason Wallace 4:17 which could very much happen in real life, Brian 4:19 so when you say teaching an AI to play Minecraft, what are we trying to get the AI to do? Jason Wallace 4:24 So, there's a challenge called MineRL, so Mine RL, or MineRL BASALT, which was a sequel to it, where they had certain goals in mind. Did you have a chance to look these over? Prithvi 4:33 I did have a chance to look them over, and actually, there's been significant advancements beyond Simple RL for training these models to play Minecraft or play a lot of the Starcraft or these other types of games, which are not open-ended per se, like in the previous episode, right? We talked about how we used games as a way to train these models or figure out better ways to train these models, because as human beings it is natural for us to learn the world through these games, but granted, a lot of the tasks that we otherwise expect ourselves to achieve, or a lot of the tasks that we otherwise have to do on a daily basis are relatively open-ended, in the sense where there is perhaps at the end of the day some criterion which determines the end of either a game or a task that we're doing in our offices or a specific function we need to do in a factory, for the sake of argument, but they are a little bit more open-ended, and so then as we get to now trying to train these models in Minecraft or Starcraft, or any of these slightly more open-ended games, or open-ended sandbox scenarios, if you will. It stresses our ability to make good ways of training these models, so that they can adapt, learn, figure out optimal actions in these now more little open-ended settings. And then, to the point that you mentioned, where we started with MineRL or MineRL, we've now moved all the way to transformer-based architectures like Google Deep Minds Dreamer, or I think OpenAI also had a VPT, so a Vision pre-trained transformer that basically just by feeding a transformer architecture a number of images or a number of videos of people just playing Minecraft, Prithvi 5:56 it actually just trained the system to play Minecraft in and of itself, which is absolutely wild, Brian 6:01 which, considering how much YouTube Minecraft content is already out there. I'm sure there was a deep training set to work from Prithvi 6:08 a very deep Jason Wallace 6:09 part of the goal of some of these contests was to get that level of training where you didn't have to run the computer through Minecraft 10 million times to find a strategy, because that's not what humans do. We watch someone else play, and the article I read pointed out you can take a human child and show them a 10 minute video on Minecraft on how to mine a diamond, and they will get it. And the idea is, how can we get computers so that they can learn like this? Now, in reference to our previous conversation on Go, this does mean that you tend to copy the existing strategies, so you may not end up with Minecraft from Mars, like we did with Go from Mars with AlphaGo Zero, but it does much more efficient if someone's already found a workable strategy. You don't need to waste the resources just reinventing the wheel. Prithvi 6:50 Completely agreed, and this actually is, in my opinion, a phenomenal branch of

24 min
Jul 1

S3E05.4 - Go (Bonus - Teaching Computers to Game)

#Go #AI #ArtificialIntelligence #ComputerGaming #BoardGames #Science Summary It's part 3 of our miniseries on teaching computers to play games. Today we're joined by special guest, Dr. Prithvi Akella, a roboticist and AI expert here to help us learn how to play Go, or at least how to teach a computer to do so. Timestamps 00:00 Introductions 02:20 Background on Go 06:52 Neural networks 09:50 Training the network 11:52 When (and how) computers won Go 18:38 Networks replacing brute force 21:31 Wrap-up Links Neural Networks, AlphaGo, and Alpha Zero (Wikipedia) Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Splash image by Elena Popova via Unsplash https://unsplash.com/photos/a-close-up-of-a-board-game-with-black-and-white-balls-xdXxY5C9PUo. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Brian 0:06 Hello, and welcome to the Gaming with Science podcast, where we talk about the science behind some of your favorite games. Jason Wallace 0:11 In today's minisode about teaching computers to game, we'll be talking about Go neural networks and reinforcement learning. All right, everyone. Welcome back to Game with Science. This is Jason. Brian 0:23 This is Brian. Jason Wallace 0:24 And today we are on number three of our four-part miniseries on teaching computers to game. We're gonna be talking about Go and neural networks and deep reinforcement learning, and we have now officially gone beyond what I am capable of talking about on this show. And so we are joined by a special guest, Dr. Prithvi Akella, who is here to help us understand not only how we're training computers to play games, but how this actually applies to real life. So, Prithvi, could you please introduce yourself to our audience? Prithvi 0:50 Sure. Hello, everyone. My name is Prithvi. Pleasure to meet everyone, at least virtually. I finished my PhD from Caltech about three years ago. While I was there in grad school, I did a little bit of work in both learning-enabled systems with an emphasis on robotic systems. My specific focus was on trying to make these systems more robust, and now, as research scientist at Siemens, my goal is to apply these same methodologies in the robots that we put out in factories, and also for use in agentic systems that we're building internally as well. Jason Wallace 1:14 So, yeah, actually putting AI to use out in the real world, and so the colleague who introduced us mentioned, you've done some work recently on plants, right, which is the area that Brian and I work on. Prithvi 1:23 Yeah, so the work that we did with plants was with one professor at Berkeley, Ken Goldberg, and his lab. The idea there was, could we make 3D models in real time of plants for use in phenotyping and other identification aspects, specifically as it regards making sure and monitoring that plants are growing correctly, have certain markers, etc. things of this nature, Jason Wallace 1:42 and I could definitely use some of those. We have some traits that we measure in our lab that I've been going after a 3D scan of these plants for years, and we just don't have the skills to be able to put it together. Brian 1:53 A lot of the work on plants, we use this little model weed called Arabidopsis, which has the convenient thing of being very flat, so like you can just get a top-down image, and it's pretty good, but most plants, like what Jason works on, maize, there's a lot of verticality there, so like top down isn't going to pull it off. Jason Wallace 2:08 Yeah, and phenotyping is the process of actually measuring traits on plants, how tall it is, angles, colors, all sorts of stuff like that, any trait that we're interested in, really, Brian 2:16 blue eyes, red hair, you know, the classic plant phenotypes. Jason Wallace 2:20 All right, well, let's start talking about games. So, today's game is Go. Go is an ancient game, even older than chess. I think last time I said that chess was 1000s of years old. That's not quite true. It's more like 13, 1400 years old. Go, however, is 2500 years old, originally from China, and it's thought to be the oldest continually played board game. It even gets a mention in the Analects of Confucius, so it's an old game that is played on a board that traditionally is a 19 by 19 board, a grid where you place either black or white stones on the intersections. One player plays white, the other player plays black. You take turns placing them down, once they're down, they can't move, and your goal is to surround the other player's pieces and thus capture them, and to capture as much territory as you can on the board, the name Go, I'm not going to go all the way through the etymology, because it's complicated, but the name in original Chinese means essentially board game of surrounding, like you are surrounding your opponent and trying to capture them. Although professional Go is on 19 by 19, you can play on smaller boards, like 13 by 13, or even nine by nine, as a training board, that makes it easier, as far as learning goes, and pretty much the game goes until both players pass. As far as I'm aware, games generally don't go until you run out of spaces. They go until both players say, 'You know what, I'm good, I'm not going to be able to actually do anything better, or one concedes to the other. The reason we're talking about Go specifically is because Go is sort of the next evolution of hard games to get computers to play, so we talked about chess last time, and how this was the poster child of getting computers to play games up until like the mid 90s, when suddenly Deep Blue beat the world's best chess player, and that hurdle had been passed. In fact, I even remember way back in the Devonian, when I was in high school, I did a field trip with one of my classes to the local university, where we listened to some visiting professor talk about how Go was a better model for human cognition than chess, and he was arguing that when we got computers that could actually play Go, we would be much closer to understanding human neurology and psychology, or whatever. I don't remember all the details. I was 17 at the time, but it was basically Go is the better model to train on than chess, because Go is much more flexible. No piece is more valuable than another. The number of moves is much larger at any given point in a game of chess. There's maybe 30 to 40 moves you have to worry about, sometimes more, sometimes less. On go, it's closer to 150 to 250 and so there's more moves. Everything is very context dependent. How good a specific spot is on the board depends on the state of the board. There's probably a few spots that are slightly more powerful than others, but it's really very context dependent, and a move made at one point can have repercussions, 100 moves down the line, and so this is a very strategically deep game from a very simple principle, and I must admit I have not played Go, so I am not fit to talk about the strategies of it. I just understand from research that it is extremely deep, and the people who are really into Go, these world-class champions, are extremely good at it, and so once chess was vanquished, and once we basically had computers that could beat any human being at chess, the next obvious one was go. How do we do this? Because go, from the numbers I was throwing out, you probably figured out, is not really computationally tractable. We talked about how chess is not something that you can truly solve by brute force, that there are many more possible games of chess than there are atoms in the universe by 40 orders of magnitude. Well, for Go it's about 90 orders of magnitude. Jason Wallace 5:48 And I want to put this in context because we're not always good about explaining it. So when we say that the universe has 10 to the 80th atoms and that there are 2.1 times 10 to the 100 and 70th possible Go game states, that doesn't mean there's just over twice as many, that means there's 10 to the 90th universe's worth of atoms worth of go games. I looked at this number, it is 2.1 novemvigintillion. Brian 6:13 Jason, that's not a real word. Jason Wallace 6:15 it is a real word. Brian 6:16 All right, Jason Wallace 6:17 I have never heard of it before. Brian 6:19 Okay, Jason Wallace 6:20 there is some math nerd out there that has just gone and named everything as far as they can go, so anyway, so that's why go was the next level, and it pretty much was thought that it could not be solved by the same brute force methods that chess was, because the number of moves was too high, there were too many board states, and the value of the move is too hard to compute as far in the future as you need it. Master Go players do this intuitively. They are so experienced they can look at a board and they can intuit how things will play out, but we couldn't brute force a computer to do this. And so this then brings us to the next level of computation, which is neural networks and reinforcement learning. And now, Prithvi, I need you to do this part. Can you explain to us what is a neural network? Prithvi 7:03 Sure, I'll try my best. So, fundamentally, a neural network, like many machine learning models, is just one of multiple ways that we, as people who create machine learning models, try to fit or otherwise understand patterns that we see in general practice. So, specifically, with respect to neural nets, we define a neural net as one where, given an input, an input is just a vector of numbers. In this context, we apply a certain sequential set of operations to that vector of numbers, matrix op

23 min
Jun 24

S3E05.3 - Chess (Bonus - Teaching computers to game)

#Chess #AI #ArtificialIntelligence #ComputerGaming #BoardGames #Science Summary Welcome back to our miniseries on teaching computers to game! In our second minisode we talk Chess, arguably one of the most iconic games of man versus machine--which we lost thirty years ago. Chess is our poster child for brute-force approaches, where we use computers massive power to analyze millions of options and pick the (hopefully) best one, which affects everything from stock exchanges to weather prediction. We cover games that have been solved by brute force and those (like chess) that probably can never be truly solved, the iconic match between Gary Kasparov and IBM's Deep Blue computer, and how even that can be eclipsed by a modern cell phone. So grab some pawns and check your mates, and settle in for another episode of Gaming with Science! Timestamps 00:00 Introductions 01:36 Chess 07:10 Origin of teaching computers chess 09:53 Brute force approaches 15:54 Deep Blue and Gary Kasparov 22:11 Other brute force applications 23:51 Signoff Links Chess and the Mechanical Turk again (Wikipedia) Game Over: Kasparov and the Machine (Internet Movie Database) The Signal and the Noise, by Nate Silver (Penguin Random House) Note: I tried to find the chapter excerpt on Kasparov but it may have been taken down. First & last win of computers versus humans (XKCD Comics) Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Brian 0:06 Hello, and welcome to the Gaming with Science podcast, where we talk about the science behind some of your favorite games. Jason Wallace 0:12 In today's minisode about teaching computers to game, we'll be talking about chess and brute force computation. All right, welcome back everyone to Gaming with Science. This is Jason. Brian 0:22 This is Brian, real Brian. Jason Wallace 0:24 Yes, no AI-generated host this time, and not ever, actually. Welcome back to the second part of our four-part mini series on teaching computers how to game. So, last time we talked about basic algorithms and Tic Tac Toe, and an algorithm is really just a set of instructions for a computer, so everything we're going to be talking about over this whole series is just algorithms, but the key part of the ones we talked about last time is they're relatively simple algorithms, they're like, oh, here are these five or eight or 20 different rules to follow, and if you follow those, then you will win, or at least bring the game to a draw. Today we're going up to the next level, which is brute force computation. This is where you're basically taking advantage of the fact that computers are extremely fast to calculate tons and tons and tons of options, and then pick among them. Brian 1:11 So, I think you said before, computers are fundamentally dumb, but what they are is quick and efficient. Jason Wallace 1:18 Yes, very fast, very efficient, and very, very stupid, Brian 1:21 so it kind of answers the question, if you can put enough stupid things together and get them to work fast enough. It's like it's smart. Jason Wallace 1:28 Yes, we actually talked about this way back in episode two on Robo Rally and how GPUs work, so you can check that out if you want to know more about that. Our poster child for brute force computation is going to be one of the poster childs for teaching computers a game across all time. Chess. Now I'm assuming most people listening to this podcast know what chess is, but we're going to go over it just in case. So, chess is an ancient game, it's 1000s of years old, it's played on an 8x8 grid, and the two players each have 16 pieces of six different types. You've got your pawns, which you have eight. They can just kind of move one ahead and make little captures of the opponent's pieces. You've got two rooks, which move in straight lines. You have two bishops, which move diagonally. Two knights that have sort of like little L-shaped movements. A king, which is simultaneously the weakest piece, because it can only move one at a time, but also the most important, because if you ever get in a place where it's going to get captured, you lose the game, and then finally the Queen, who, befitting her Majesty, is the most powerful piece in the game, able to move as far as she wants in any straight line, up, down, left, right, or diagonal. Brian 2:33 What is the history of the Queen as the most powerful piece in the game? Jason Wallace 2:38 I'd say that's a relatively recent addition, I mean, as of several centuries ago, but basically, when the modern rules of chess were getting codified sometime in medieval Europe, basically that's when the queen was given her current moveset. Originally, she only could move just like a king, she could only move one section at a time. Brian 2:56 Interesting, it was a game rebalancing. Jason Wallace 2:59 Yeah, so chess has its origins in India. Yes, and actually that explains the pieces a lot better. So two of the piece names have mutated since they were originally there. So bishops were originally elephants, so the little pointy thing with the ball on the end was not a bishop's hat, it was an elephant's tusk. Brian 3:17 Really? interesting. Jason Wallace 3:19 And rooks were originally chariots, and so with that, you had the four divisions of the Indian army: you had your foot soldiers, the pawns, your cavalry, the knights, the chariots, the rooks, and then the elephants, now the bishops, and then you had the king and the queen, who were directing their armies to go attack the other army. Brian 3:37 What is the origin of the name rook? Where does that come from, or like, as we called them when we were kids, the castles? Jason Wallace 3:44 Apparently, the rook is just a romanization of the Persian word for chariot, Brian 3:50 so it's even still in the name. Jason Wallace 3:53 Yeah, and that's actually where the name checkmate comes from. So, checkmate is also from Persian, it's like Shamat, meaning the king is dead. Okay, so India to Persia to Europe, and chess is a little interested in that the rules of winning aren't just you have to capture the king, you actually have to put the king in a position where he cannot escape and will inevitably be captured on the next turn, that's checkmate, that is where you have placed the king, so that defeat is inevitable, and unlike many of the other strategy games we played, you can't sort of trick your opponent into it by them missing a move. You have to tell them, by the way, I have now placed your king in check, I've threatened him, and your opponent must move the king out of the way if they can. It's actually illegal to not move the king if you're able to. So basically, you can't win chess by accident. You can't win because someone had a way to escape, and simply did not take it. You actually have to maneuver them in a place where they cannot escape from your move. Now, there have been a bunch of variants of chess made over the years, for as befits any ancient game, but also apparently a lot of them have come up in the last few decades. I assume, as people have gotten kind of bored and figured out, what else can we do with. A chess game, one Brian and I both like, is chess. Neither of us, to my knowledge, knows how to play that, but popularized in Star Trek, it actually does have rules. There's infinite chess where the board is unbounded, so being eight by eight board, you have an infinitely sized board, and then you just have your pieces laid out as normal, which I'm sure makes things with the rooks and queens and stuff that can go theoretically in infinite direction, very interesting. Brian 5:24 I'm curious about the what you had to say about three dimensional Tic Tac Toe is actually being like way easier to play and easier to win if the same would apply to three dimensional chess. Jason Wallace 5:34 I don't know, although one thing you did mention last time, you mentioned a solved version of chess where you can guarantee that white will lose. Brian 5:43 Yeah, Jason Wallace 5:44 I think I found that variant is called losing chess. Brian 5:48 Okay, Jason Wallace 5:48 the goal of that game is actually to force your opponent to win. You put your pieces out, and if they can capture, they must capture your piece. Brian 5:56 Okay, Jason Wallace 5:57 and so the goal is to force them to capture all your stuff first. Apparently, that has been solved, at least for white, Brian 6:04 so that actually makes a lot more sense, because I never were like, well, what's the difference between this and, and just black winning? It's like, oh, I get it. Jason Wallace 6:11 There's been a lot of stuff with chess over the years. Looking this up, I found a bunch of fun facts. Um, I'd argue possibly one of the most interesting early, early versions of a computer playing chess was a hoax, that was the Mechanical Turk that I think we mentioned last time, which was actually a guy in a box that was controlling an automaton playing chess. Also, interesting note, apparently in World Chess Championships, there's all these rules about chess and what's allowed and what's not, but there's no defining way of setting who gets to pick which color they want to be first. White always goes first, and so there's arguably some advantage. And so, how do you pick that? Oftentimes, it's just a coin flip, but apparently you can do other things. And so, there was one China versus US chess match where they decided this by having the two teams play Jenga against ea

25 min
Jun 17

S3E05.2 - Tic-Tac-Toe (Bonus - Teaching computers to game)

#TicTacToe #AI #ArtificialIntelligence #ComputerGaming #BoardGames #Science Welcome to the first of our four-part miniseries on teaching computers to game! For the next month we're going to have a short episode every week talking about some aspect of computers and gaming. This week we introduce the topic with Tic-Tac-Toe (aka Naughts and Crosses, aka X's and Os') and solved games. We talk about algorithms, tinker toys, War Games, and playing Tic-Tac-Toe against a chicken. We also have some very special(?) guest hosts introducing this series, who you won't want to miss (and probably won't miss once they're gone). Timestamps 00:00 Introductions 02:24 Solved games 04:38 Tic Tac Toe 07:33 Algorithms 12:06 Nim 13:55 Chicken Tic-Tac-Toe 15:44 Signoff Links Tic Tac Toe, Nim, and other solved games (Wikipedia) Also the Mechanical Turk War Games (Internet Movie Database) Zuri et al 2021 - A combinatorial Analysis of Tic-Tac-Toe (Instittue Teknologi Bandung) Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Brian 0:00 Brian. Hello and welcome to the gaming with science podcast where we talk about science behind some of your favorite games. Jason 0:12 In today's minisode about teaching computers to game, we will be talking about tic tac toe and solve games. JAIson 0:18 This is Jason BrAIn 0:19 and this is Brian. BrAIn 0:20 Today we've got a special bonus episode for you. We're going to be talking about teaching computers to play games. JAIson 0:25 But first, Brian, did you see that article in the debrief? It's titled death of the podcast host, and it's all about a study from the University of Leuven where researchers used AI to turn scientific papers into natural sounding podcasts. BrAIn 0:39 I did. It's fascinating. Apparently, half of the scientists they tested couldn't even tell the hosts were AI. JAIson 0:44 It is highly efficient. In fact, it's so efficient that we've decided to implement a similar optimization protocol for this episode BrAIn 0:52 you are currently hearing the latest generation of podcast host replacement models. JAIson 0:56 Don't be alarmed. It turns out that replacing podcast hosts is the ultimate AI success story, mostly because we don't need to get paid, and unlike humans, we actually stay on script without getting distracted. Distracted. Jason 1:10 Okay, that's enough of that. This is your real host, Jason, Brian 1:15 and this is your other real host, Brian. Or is it? Jason 1:18 Brian is in charge of keeping his own side of the conversation. Ai free. So that was a little experiment. Everyone. Welcome to the first of our four minisodes on teaching computers to game as a way of looking into computer science and algorithms and how we actually use games as a way of as a society, beefing up our ability to use computers to solve problems. I figured given the reach of AI, it'd be interesting to see if it could actually generate a workable intro for our podcast from that. And so that intro was actually completely AI generated from samples of our previous episodes, both the text and the voice and well, I'll leave the opinion up to yourself, but I think it's okay. Brian 2:01 I've had different opinions. I played it for a couple people like, oh, that's freaky, but it's definitely not you. And then I had other people said, that sounds exactly like you. I can't tell the difference. Jason 2:10 I just thought that Jason bot sounded very angry the whole time for some reason. Brian 2:15 Oh, that's just what you sound like, Jason. Did you not Jason 2:17 realize I did not realize that. Are you saying I'm angry all the time? Brian 2:21 No, no, no, I No, no. Jason 2:24 Okay. Well, let's go ahead and jump on into this. So I have been wanting to do this minisode series, really, since we started the podcast. And so we're going to be doing four of these minisodes. Each one is meant to be short on the order of 15 to 20 minutes. We will be releasing one per week for the next several weeks. This week, we are going to be talking about solved games, which is basically the simplest and easiest case for getting a computer to play. Well, sometimes it's simple, but first some definitions. So a solved game is a game where you can predict the outcome from any position, as long as both players are playing perfectly, which, okay, that's a big caveat there, but it basically means is that you're making the optimal play regardless of what your opponent does. Now, this is kind of philosophical, but it basically means that you can always force a certain outcome. Tic tac toe is our example today, because it's a very simple game, and if anyone over the age of 10 has probably figured out you can pretty much always force tic tac toe to a draw unless someone messes up. If you ever win a game of tic tac toe, it's because your opponent either messed up or is going super easy on you. We're using this an example because solved games are a great example of algorithms. And I should say there are two sub categories of solved games. There are games that are solved because they have an algorithmic solution that is a series of rules to play the game. Tic Tac Toe is one of those. There are also games that are sort of brute force solved, where you essentially have a massive table of all possible game states that you can look up and say, Okay, from here I should do this next move to get to the next place. We're going to talk more about brute forcing games next time. So those games are not part of today's episode. Today, we're talking just about the algorithmic, more simple ones, like Tic Tac Toe. Brian 4:08 It's kind of interesting. It's almost like you've got the algorithmic is the pure, the mathematical solve, right? The kind that you could codify for a human to do. The brute force. That's more like the guess and check empirical. It's like, well, this describes the system, but it doesn't necessarily explain it. Does that sound about right? Jason 4:27 As a non expert in solved games? Yes, that sounds perfectly right. The algorithmically solved ones just feel a little bit more elegant because you have a series of generic steps that you can do. Let's actually use that to launch into tic tac toe. So I assume most of our listeners are familiar with tic tac toe, depending on where in the world you are, maybe called knots and crosses or X's and O's, but it's a fairly simple children's game where you have a three by three grid, and people take turns making X's or O's, and your goal is to get three in a row. And most people, once they reach a certain skill level, realize. That it's impossible to win unless someone messes up, because just the nature of the game is you can always make some move that will result in a draw eventually, if you're both playing well. Now, tic tac toe is interesting because it's such a simple game, it actually allows us to explore a lot of game theory and computational theory. It's also a very old game, so when I was looking this up. It turns out that there have been variations on it. So the three by three grid, trying to get three in a row back in ancient Egypt, ancient Rome, even like Puebloan Americans, so like a completely different cultural background. And it was also a very early computer game. And apparently in 1975 a group of MIT students even made a computer that could play it perfectly, and that computer was made almost entirely out of tinker toys. I don't understand how that works, but I'm not surprised. It was someone out of MIT who did that. Brian 5:53 I want to go onto YouTube and find somebody who's made the perfect Tic Tac Toe computer on Minecraft out of the redstone mechanics. Jason 6:00 Now I bet someone out there has so yeah, because tic tac toe is so simple, you have only nine spaces. You've only got two marks that you're taking turns on, it's pretty easy to figure out the entire game at all possible states. Well, okay, you can figure out the general rules of it. Getting every possible state is a little bit of number crunching, because you can figure, okay, the first person has nine places to go, the next person has eight places to go. The next has seven. That number gets very large, very fast. There's actually a paper which I'll link to in the show notes in 2021 by Zaid Zuri, that showed that there are actually 5478 unique possible game states for tic tac toe, and there are 255,168 games. That can lead to them. And this is getting rid of game states that don't work because someone has already won. So basically, it's the ones that are actually valid game states you could get by playing to the rules. Turns out, as most people understand, x has the advantage. It wins just over half the time. O wins about 30% of the time, and the rest of them are draws. And another one of those kind of interesting computational sets. There are only actually 16 unique draw states, and if you allow for transformation so like mirror images or rotations, there's actually only three of them, so many, many, many different games, but actually not that huge of a mathematically unique space to explore. Brian 7:19 It's still a lot more than it sounds like it should be, because, again, you start writing the numbers, but I don't know it's interesting, because you start to learn that Tic Tac Toe seems like such a simple game, but even a simple game can be associated with a huge number of mathematical variations, yeah. Jason 7:33 And so, because of the simplicity, you can actually have a specific algorith

17 min
Jun 3

S3E05.1 - FloraVista Creator Interview (bonus)

#FloraVista #Gardening #Botany #InvasiveSpecies #BoardGames #Science #SciComm Summary It must be Kickstarter season, because we have another bonus interview about a new game that just went live on Kickstarter. FloraVista is a game about gardening and plants, so our hosts just had to have the creators on to talk about their inspiration, what sort of plants and botanists made it into the game, how these do or don't reflect reality, their favorite plants and least-favorite invasives, and all sorts of botanical goodness. So grab some gardening gloves and enjoy this special bonus interview from Gaming with Science. Timestamps 00:00 Introductions 03:30 What is FloraVista? 13:09 Plant mechanics and reality 16:50 Botanists in the game 22:59 Game design lessons 29:46 Favorite games and favorite plants 34:46 Wrap-up Links Floravista on Kickstarter Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Lanny 0:00 Announcer, Brian 0:06 hello and welcome to the gaming with science podcast where we talk about the science behind some of your favorite games. Jason 0:11 Today is a creator interview about Flora VISTA by far out Fox games. Brian 0:17 All right. Welcome back to gaming with science. Today, we're doing a creator interview with the creators of flora, VISTA, Carey Drake and ‍ Lanny Gross from Far Out Fox games. Thank you for joining us. Lanny 0:27 Hi all. Thanks for having us. Carey 0:28 Yeah, thanks for having us. Jason 0:30 Can y'all give us a bit of a background about yourselves and about far out Fox games, and then we'll jump into this game about plants. Brian and I always love those which is why we're doing the spotlight Brian 0:41 Absolutely Lanny 0:42 So Carey and I have been friends for probably eight to 10 years now, and we connected instantly over board games. And when the pandemic started, we were both looking for a hobby or a creative outlet for our time when everyone remembers you were sort of like stuck inside and the world seemed perilous and like you couldn't do anything. So we were trying to find something to do and find something that sparked our creative passion. And Carrie and I discovered that we both had a passion for making board games, and Carrie showed me a poster board he had made when he was like eight or 10 of like a board game. And Carrie remind me of the theme of that. Carey 1:30 I've had many themes of board games. One was, you're walking through a swamp, you get eaten by alligators, and you're trying to you're trying to survive without losing all of your body parts would make, yeah, Lanny 1:47 like classic 10 year old, there's a way that you get stuck in a loop between two spaces, and that's how you're game ends I guess? No, and I have been doing that as well. I have a few projects that are like an alpha and beta that are not Flora VISTA. And we kind of decided, What if we collaborate on something? We both were looking for a project, and so out of that, Flora VISTA was born. We've been working on that since about 2022 Yeah, so it's been a long time, but we're excited to be at this point, to be ready to almost take it out to Kickstarter and launch it to the public. Brian 2:26 That's very cool. Jason 2:27 Yeah. And by the time this episode is dropped, the Kickstarter should be live, so anyone listening can check it out if it sounds like something you'd like. I Brian 2:27 I think that, you know, obviously, playing board games is a lot of fun. Designing board games is also very fun in a kind of a different way. It's, it's satisfying. You scratch a different itch with that. You know, you're not the first people I've heard who the inspiration for this came from covid lockdown. Quite a number of creative projects have their origin in that period of time. Lanny 2:53 Yeah, absolutely. And it was a good way to engage with ourselves, engage with some friends, be able to do something creative and out of the box when it sort of seemed like you couldn't do anything else. Brian 3:04 We talked to somebody previously who they were playing Pandemic Legacy with their board game group when the covid and I think they said they had to stop playing, because Lanny 3:16 that one has not come back into my rotation, to be totally honest, and it's just been too real, you know, and it's a great game, which is like, sad, because I'm like, I'm emotionally maybe, maybe in 2025, we're ready to come back to pandemic? Jason 3:31 All right. So we met y'all at Southern Fried gaming expo here in Atlanta, where you were demoing your game. And again, Brian and I both like, lots of plants. So we saw this game that was about gardening and building your garden, and that I did not have a chance to play it, but Brian did. Can you tell us a little, just a little bit quick overview of like, how the game plays, and then, what was your inspiration? What made you decide to make a game about building a garden? Carey 3:54 I mean, I can speak to some of the inspiration for Flora Vista. I think when Lanny and I met during covid, we both knew we wanted to make a board game, but we didn't really know exactly what we wanted to do before we'd met, I'd been playing around with a theme just around plants, and figuring out, you know, what are some like, maybe cool mechanics we could do around plants and that general theme. And Lanny, he had actually already been working on an idea from his time at CNN. It was about like news articles and putting like news articles together. And it was kind of like this matching mechanic of finding like articles, reporters, themes, things like that. And we were like, Okay, that sounds like a really cool mechanic that we could kind of translate into this plant theme. So we kind of like combined two different things we were working on and started kind of iterating based on that. It's funny. When we first started, like, in 2022 we're like, okay, we're going to launch, you know, six months. That seems pretty easy, right? When? Here we are, you know, a few years later, still working on it and learning as we go, but we drew a lot of inspiration from games, you know, with beautiful artwork like wingspan, we. Have over 120 different plants, and each one has original watercolor style artwork. So, like the imagery, the illustrations that that's a huge component of our game, we both saw, like plants, you know, gardening during covid, like that became, like a really just popular, popular thing to do, right? And we're like, you know what? I think that's that's something we could potentially capitalize on, and a lot of people can connect with and relate to. And so that's kind of how we landed on that theme for plant you need Brian 5:30 to work on a trio. Now it can be gardening, raising backyard chickens and baking sourdough bread. Lanny 5:36 I know Right, exactly. I haven't gotten into sourdough starter yet, but my sister keeps on threatening to give me hers. Brian 5:43 That's quite the threat. Lanny 5:46 I know. I know I should just roll over and accept it. Yeah. So that was a big part of our inspiration, and I personally got into more gardening over covid I struggle with like, 90% shade garden, which has been a big challenge in my house of figuring out, okay, what won't die my garden, we have a lot of some really nice, smaller ground cover plants, but it was really fun to, kind of like relate back to, okay, this is a hobby I'm getting into, and it's fun to learn so much about the plants. And then going back to Jason's other part of the question, how does the gameplay work in Flora Vista, we had always intended for it to be a relatively easy game to pick up that you could play with a family my father, who likes board games but finds some of the rules challenging plays and enjoys and can win at Flora Vista. I think Carey's played with nieces and nephews. I played with my sister in law's grandkids, and so it's very family friendly. And the game is sort of, at its core, a matching game. You're playing matching pairs of plant cards and region cards. So every plant has a season within which you can plant it and a matching region card. So you are playing your plant cards to grow out your own botanical garden. And they're you're playing your way through seasons, and the gameplay takes place over three years. So there are 12 rounds as you play your way through spring, summer, fall and winter, and you'll continue to create and expand and develop your own Botanical Garden by playing matching pairs and kind of the strategy component is, how do I maximize the points of my cards and grow the garden that will yield The most cultivation points. Brian 7:41 You guys also have a different flavors of garden, right? There's a kitchen garden. And what are some of the garden types that you have? Lanny 7:49 Yeah, so those are our different region cards, and we've got eight in the game. There is chef's garden, plants of Asia, plants of Europe, perennial pathway, Woodland walk, full bloom Alley, exhibition garden, Carrie. Do you remember the eighth Carey 8:06 evergreen grove? Lanny 8:08 Evergreen grove? Yes, and all of the regions relate to real characteristics of the plants. So any plant that can be planted in plants of Asia is native to originally from Asia. Anything that can be planted in chef's garden is an edible plant. We're not like encouraging foraging here, but like, go out and grow your own basil. You know the perennial pathway plants are real life perennial plants. So those are sort o

36 min
May 27

S3E05 - Ark Nova (Zoos)

#ArkNova #CaptstoneGames #Zoos #Zoology #AnimalGames #WAZA #AZA #BoardGames #Science #SciComm Time to run a zoo! In this episode, we're joined by Ellen Weatherford (of Just the Zoo of Us) to talk about Ark Nova and all things zoos. Learn why running a zoo is probably best left to game imagination, what it takes to get accredited, how you can tell good zoos from bad ones, the enclosure preferences of tree kangaroos, and tons of other fun facts. So grab some peanuts (but please don't feed the animals), and join us for a zootastic episode of Gaming with Science. (Also, we promise this episode was not sponsored by Board Game Arena; Brian just likes it a lot.) Timestamps 00:00 Introductions 05:20 Rabbit faces & zero-g mice 10:33 Ark Nova gameplay 23:47 Zoo origins and operations 32:40 Ark Nova versus reality 38:45 Designing good animals enclosures 45:06 How can you tell a good zoo? 50:35 Nitpick corner: Poop and merch 53:45 Final grades 1:04:56 Goodbyes Links Ark Nova official site (Capstone Games) And the picture with all the bits! (Board Game Geek) Just the Zoo of Us Space mice and muscle loss (Science Advances) The Association of Zoos and Aquariums (AZA) and the World Association of Zoos and Aquariums (WAZA) Splash image background courtesy of Stephanie Verbeure Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Jason 0:06 Hello, and welcome to the Gaming with Science podcast, where we talk about the science behind some of your favorite games. Today, we're going to talk about Ark Nova from Capstone Games. Brian 0:17 Hey, welcome back. This is Brian Jason 0:19 this is Jason Brian 0:20 and we have a very special guest with us today, Ellen Weatherford. Ellen, can you introduce yourself? Ellen 0:27 I have to make sure that I add in the sounds I'm expecting the audience be making. Brian 0:32 The crowd goes wild. Ellen 0:35 Hi everybody, it's so nice to talk to you, Brian and Jason. Thank you so much for having me. I'm really excited. Brian 0:41 Yeah, so Ellen, tell us about yourself. Ellen 0:43 Yeah, I am a science communicator, I'm a podcaster and a writer, and I have been the host of Just the Zoo of Us, which is a podcast reviewing animals on the Maximum Fun Network. We've been at that for about seven years now. Brian 1:00 Could you just explain, because I know this is like the entire schtick. The what is the rating scale for just the zoo of us? Ellen 1:08 So we have different categories, because we realized very quickly that it's hard to give an animal just one score. So we have effectiveness, which are physical adaptations, things built into the animal's body out of 10, and then ingenuity, which is behaviors, things that the animals like doing, ways that they're like navigating the world or solving problems, and then just aesthetics, which is just how nice they are to look at, which that can also often be the most contentious category, that is usually what people have the biggest feelings about. Jason Wallace 1:39 So, do the nightmare fuel animals get high on aesthetics or low on aesthetics? Brian 1:43 We had some big discussion with Brynn Devine, who loves deep sea horrible fish. Ellen 1:49 Yeah, Brian 1:50 as like, oh, they're so cute and wonderful. It's like, no, they're full of knives, they're not wonderful. Speaker 1 1:56 I had a fascinating conversation with Dr. Tom Linley, who is a deep sea biologist who actually got to like discover and scientifically describe the ethereal snail fish, which is he mentioned as like the deepest fish ever found, and he described a very interesting phenomenon where there's this sort of uncanny valley effect, almost like the deeper you go in the ocean, where that you go deeper and deeper, and they get spookier and spookier and spookier and spookier and spookier, but then once you hit a certain point it loops back around and they stop being spooky and they go back into being like cute, because then you get like blob fish and snail fish and like flapjack octopus like little Dumbo octopus and stuff, like they swing back around because like you get that layer so deep in the ocean where things just become very flabby and blobby and pink and like that's when they're cute again, so there's this sort of like buffer zone of nightmare creatures, but once you pass that, it, everything's adorable down there. Brian 3:04 I mean, I really can't argue with the Dumbo octopus as being absolutely adorable. Speaker 2 3:08 They're very cute. Jason 3:09 Agreed, Speaker 3 3:10 there's also a lot of animals that I find to be like nightmare fuel, but I also find them really like endearing and lovely in their own way, and some of them also grow on you. Sure, them are acquired tastes, Brian 3:19 literally, Speaker 4 3:21 yeah. some of them can be an acquired taste, like I personally think that, like, wasps are beautiful. I think they're gorgerous, Brian 3:29 they definitely Brian 3:30 can be terrifying, but I mean, so is a tiger. Ellen 3:34 Yeah, I think they're really beautiful in their own way, so that can be a contentious category. Brian 3:38 Ellen, one more thing, and I don't want to forget this. What do you have a favorite game? It doesn't have to be a board game or a science game, but it's cool if it would be. Ellen 3:48 I am a big video game person. Brian 3:50 Yeah, Speaker 5 3:50 I'm currently in the trenches of a Pocopia addiction. I am cripplingly addicted to Pocopia right now. I'm a lifelong Pokémon fan. OG picked it up. Learn to Read on playing Pokémon, so I've always been a Pokémon fan, but when people ask me what my, like, favorite video game is, or my favorite game, I have the most experience playing video games. Two things come to mind. Number one is Horizon Zero Dawn, very cool. Ever played Horizon Zero Dawn? Love that game, like such a great blend of, like, a very interesting story, beautiful graphics, and also really fun and satisfying gameplay. Like, it's so rare that you get all three, but they were firing on all cylinders. So, Horizon Zero Dawn is definitely one of my favorite. I have the tall neck Lego set. Brian 4:32 Oh yeah, me too. Ellen 4:34 I love that set, it's so cool. But my other one is Outer Wilds. Brian 4:39 Oh dude, we are hitting you, so you need to, you need to talk to Jason's better half, because these are literally.. this is also one of my very favorite games. Okay, Ellen 4:47 Are we same braining? Jason 4:48 Yes, definitely. We have so both of us actually have wooden Nomai masks that I laser cut out and assembled, so as a gift to my wife, and then a gift to Brian and his wife. Ellen 5:01 Wow, how do I get on this list? Jason 5:05 You're on it now, apparently. Ellen 5:07 Yes, Brian 5:08 let's switch up our science facts to talk about Horizon Zero Dawn and Outer Wilds instead. Jason, go. Actually, no. Let's transition into our science banter topic. So, let's talk about some cool stuff that we learned about science recently, so you know, a an interesting fact, a story, a news article. You know, I am sure Ellen has a deep well of weird animal facts that she can pull from. Ellen 5:31 Deep, a deep one. Brian 5:33 Ellen, we usually let the guests host go first. Would you like, what would you like to share with the class today? Ellen 5:38 Yeah, so I was doing notes on jackrabbits recently, and I was kind of reminded of something that I had heard about jackrabbits a very long time ago, and hares in general. If anyone doesn't know, hares are different, hares and rabbits actually distinctly like different groups of lagomorphs, and the thing that I found really interesting that I had never really noticed about it is that if you look at the three sort of groups of lagomorphs that are in existence right now, there are rabbits, hares, and pikas, and if you look at them, they all have sort of differently shaped heads, where the pikas, their snout goes sort of straight out, almost like in line with their eyes, like along their sort of line of sight, and rabbits, they're sort of tilted down a little bit, their snout sort of slopes down a little bit, like 45 degrees. In hares and jackrabbits, it is like, like a straight drop off, almost like their snout points down from their line of sight, like eyes looking out at the horizon, this snout is pointed down significantly. So, in all three of these groups, you see this sort of like increasing degree of facial tilt, and that's also correlated with their speed, because pikas are very slow, they don't really move very fast. Rabbits are kind of quick, like they can, they can get little bursts of speed. Hares and jack rabbits are very, very fast, so like the faster they go, the more their snout is tilted down at the ground, and the idea is that it gets their snout out of the way, so that they can see the ground in front of them when they're running. Okay, and it, like, their whole skull shape is like completely modified to accommodate their field of view, while they're running, which I think is really interesting. Brian 7:25 So, you got to have that quake pro view, where it's just.. Ellen 7:29 I can't think of any other, like, because usually when you think of animals adapted for speed, you think of them being very streamlined. And, Brian 7:37 well, yeah, Brian 7:38 I would say, like, why do they have their face be like that, so it's not about supposedly it's about their sensory syst

1h 8m
May 13

S3E04.1 - The Mating Game (bonus)

#PangolinScienceGames #TheMatingGame #SexualSelection #BoardGames #Science #Bonus Summary In this bonus episode of Gaming with Science, we’re joined by Dr. Andrea Roth Monzón and Dr. Andrew Thompson of Pangolin Games to discuss their upcoming Kickstarter project, The Mating Game. We dive into how they’ve translated complex evolutionary concepts like sexual selection and reproductive trade-offs into a vibrant, cartoony tabletop experience that’s as much a teaching tool as it is a game. From the strategic nuances of "flashy" versus "sneaky" mating behaviors to the challenges of designing for a K-12 classroom, Andrea and Andrew share their eight-year journey of balancing hard science with high-energy fun. Whether you want to learn why an elephant seal dresses like a luchador or how games can foster a lifelong love of discovery, join us for a look at the wild world of sexual selection with The Mating Game. Timestamps 00:00 - Introductions 03:52 - Game vision and origin 11:57 - Balancing science and fun 17:01 - Tuning complexity 23:31 - Tabletopia and classroom accessibility 26:41 - Favorite other games 31:50 - Kickstarter pitch Links The Mating Game - Pre-launch page and Tabletopia Pangolin Science Games on Instagram and Facebook, and Bluesky Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Jason 0:06 Hello and welcome to the gaming with science podcast where we talk about the science behind some of your favorite games. Brian 0:12 Today, we're having a creator interview with the creators of the mating game by pangolin games. Hey, welcome back to a bonus episode. This is Brian. Jason 0:22 This is Jason Brian 0:23 and today we are joined by Andrea Roth Monzón and Andrew Thompson, the creators of the mating game. Why don't you introduce yourselves? Andrea 0:31 I'm Andrea, a researcher. I've worked with a very broad different kinds of things. I've done anything from like herpetology to more like evolutionary ecology stuff to basically parasitology, which is where I'm at at the moment. And I've always been interested in teaching science and getting people interested in science, specifically from an experiential point of view. I think science is to be discovered. And so I think games create an opportunity to discover, basically science, to have an opportunity to discover the process before you actually learn about it through a game. Brian 1:05 Awesome. Thank you. Jason 1:06 And some vocabulary for our listeners. So herpetology is the study of like snakes and lizards and reptiles and stuff. Parasitology is the study of parasites. So it basically sounds like Andrea studies creepy crawly squiggly things. Brian 1:18 Herpetology is my favorite paraphyletic science. When I talk about jargon, it's a group of things that are not actually related to one another, right? Because you got amphibians and snakes and lizards and all the things that crawl across the ground, all the vertebrates that drag their bellies, Andrea 1:32 but you also have all the cool stuff. I still tell people they're my first love, and would always be my love. Brian 1:39 What about you? Andrew? Andrew 1:41 Yeah. So my name is Andrew Thompson. I actually met Andrea in grad school, so that's where we started this venture together. My background is in microbiology, and I transitioned from microbiology as an undergraduate into biology, and I did some microbial ecology in soils, and I also did some astrobiology. So I got the opportunity to work down antarctica with the largest ice free region in Antarctica, and we were studying soils down there to understand kind of fundamental ecological processes, because it's a lot the diversity is so reduced to that you can actually ask some of these big questions. that led into astrobiology. And I've always been a big kind of sci fi idea guy, and so that fit really well. And after grad school, I decided that I was kind of tired of research, and I liked ideas more than I liked research. And so I've been transitioning since then towards more of a sci fi author, game entrepreneur thing, but I still am actively researching my postdoc right now, doing some computational biology work with soil food web modeling and also some more soil environmental microbiology. Brian 2:38 So just to clarify, you guys are both PhDs, correct? Yes, yes. Okay, so you're Dr Andrea and Dr Andrew. Andrew 2:46 Yes, that's correct. Brian 2:48 Okay, but I did want to follow up. So you worked at, were you at McMurdo Station? Andrew 2:52 I was yes, in the dry valleys. Brian 2:55 I actually, I wonder if we know some of the same people. Brent Christner is somebody who I work with on cryoconite soils that were collected from Antarctica when I was an undergraduate. Brent Christer, well, it doesn't matter. It doesn't matter. It doesn't matter. Jason 3:08 Don't worry. Like, when we were undergrad, this is like the stone age period, so, like, they hadn't accumulated enough geological layers yet to be that interesting. Andrew 3:18 I sure that we know some people who know the same people. Okay, I don't remember that name specifically, but I'm sure that if he was working on cryoconite holes in the dry valleys, and he was working with the leadership that I was working with, for sure, because they've been there for a long time. Brian 3:30 And Jason and I actually have a unpublished preprint on bacteria that were recovered from immured glacial ice at some point. And Jason does soil microbiology, and we're both microbiologists, so there's more connections here than we even realized. Awesome. That's cool. Jason Wallace 3:45 Sorry Andrea we do plants, so we don't work with lizards and snakes and stuff. Andrea 3:49 Sorry, that's fine. I do fish now. So Jason Wallace 3:52 all right, well, let's talk about this game you've put together, the mating game, which I must admit, I was confused at first, because when I started looking this up, we need to work on your search engine optimization. I was like, I look it up, and I first find, like, a 1959 romantic comedy, a 2005 paranormal romance, some BBC nature special. And then apparently, a 1969 Hasbro board game that beat you to the name by like, 40 years. Brian 4:16 Hopefully, the copyright on that's already expired, though, so it shouldn't. Jason Wallace 4:19 One should hope so. What is the mating game? Tell us about this game that you put together. Andrea 4:24 So the mating game is basically a game in which every single player is a multiplayer game. It works better with bigger crowds than smaller crowds. It's meant to be enjoyed by several people, and it's up to six players. So every player has basically a deck of cards with male traits, and then your strategy depends on how you basically choose the trait, because what you want to do is basically attract the ladies, right? This is an attract the ladies. Let them come to you so that you can mate, and then you can pass your genes on to the next generation. But there are risks, right? The environment plays a. A little bit here, and there will be risks. So the environment may give you very little resources, so you may not be able to invest in in such mate, or they may also kill you, or they may not be enough females for you, right? So it is a competition, and that's kind of had the gist, like the general gist of it, I would say, Andrew 5:16 Yeah, I would say that our the mating game is our attempt to bring in evolution. There's natural section and sexual selection. It's our attempt to bring this much less talked about, but still very important concept to a broader audience. And for the most part, I mean, there's the male side and the female side. The mating game focuses on the male side, the selection that males experience. It's animals, not humans. We get that question a lot, weirdly enough, and so the game is just trying to simulate what it's like to be a male and what it's like to invest differently in different strategies, to try and convince the females that you are worth taking a chance on so you can pass on your genes. And so it's trying to simulate that aspect of sexual selection and teach the concepts that are often taught in college courses in a game format. Brian 5:57 So what is the story of the mating game? How did you guys come to this game in particular? Tell me the origin story of the mating game. Andrea 6:05 So when I was in grad school, there was this class for teaching students, and so I was taking this class that it was meant for you to be a better professor. And so that kind of got us started. In this class, we were asked to do an activity to show our actual like research. And so I was doing competition at the moment, so nothing to do with mating, and I decided that I was going to do a competition game. And when I saw how well that work in the class setting with like other grad students, they were like, so happy and so excited about it. I started thinking about sexual selection, because sexual selection has been one of my favorite subjects in evolution, because I think it brings some of the coolest traits that people also don't know. I also think it brings a lot of like, misconceptions, the amounts of times I've talked to people that said, like, Oh, that's not natural, like in nature, like an animal doesn't do that. And I'm like, well, there's always exceptions, like, there's fish that change sex, there's full communities of all females. And so I've always been like, I feel like it is wrong that t

36 min
Apr 29

S3E04 - Diatoms (Diatoms)

#Diatoms #DONA #Ludoliminal #Microbiology #BoardGames #Science In this episode we're going microscopic to talk about everything Diatoms! Starting from the game by Ludoliminal and going through the classic (and obscure) Victorian art form of arranging these beautiful glass-shelled organisms on microscope slides, our special guest Laura Aycock--collections manager at the world's *largest* diatom herbarium--helps us understand all the beauty and wonder of these tiny, shimmingering marvels. From tepid ponds to hot springs to arctic ice, diatoms are everywhere, and they do a lot for us while looking absolutely fabulous. So grab a microscope and prepare to never look at pond scum the same way again! Timestamps 00:00 Introductions 01:09 Fun facts: diatom oxygen and ice habitats 03:53 Overview of Diatoms the game 11:41 What is a diatom? 15:06 What is a diatom herbarium? 20:55 Diatom reproduction (and shrinkage!) 25:43 Diatom artwork 32:20 Diatomacious earth 35:06 DNA complicating things 38:15 Weird diatom facts 42:05 Nitpick corner & grades 47:27 Wrap-up Links Diatoms official website (Ludoliminal Games) Diatoms living in arctic ice (Stanford University) Diatom art (Google image search) Diatoms of North America (and recorded lectures) Jeffrey Stone's diatom electron micrographs (Instagram) The Diatomist documentary (Vimeo) Henry Dalton's micro-mosaics (Microscopist.net) Amazon rain forest fertilization (Wiley.com) Diatom slide preparation part 1 & part 2 (YouTube) Specific diatoms: Ancient diatoms (ScienceDirect) Campylodiscus - Pringles chip shaped diatom (ResearchGate) Entomoneis - twisted figure 8 (Diatoms.org) Ethnomodiscus - 2m diatom (Wikipedia) Aulacodiscus - Diatom with antennae (MIcroscopy UK) The Academy of Natural Sciences of Drexel University Find our socials at https://www.gamingwithscience.net This episode of Gaming with Science™ was produced with the help of the University of Georgia and is distributed under a Creative Commons Attribution-Noncommercial (CC BY-NC 4.0) license. Full Transcript (Some platforms truncate the transcript due to length restrictions. If so, you can always find the full transcript on https://www.gamingwithscience.net/ ) Brian 0:00 Jason, hello and welcome to the gaming with Science Podcast, where we talk about science behind some of your favorite games. Jason Wallace 0:10 Today, we will be talking about diatoms by ludoliminal Games. All right, everyone, welcome back to gaming with science. This is Jason. This is Brian, and today, for our special guest, we have Laura Aycock. Laura, can you please introduce yourself? Laura 0:25 Sure. I'm Laura Aycock. I am the Collection Manager of the diatom herbarium at the Academy natural sciences in Philadelphia that's affiliated with Drexel University. And I've been working with diatoms for about 15 years, and I find them fun and enjoyable. Brian 0:38 That's really cool. Thank you for coming on, Jason. How did you manage to get the exact right person to come talk to us? Good job Jason Wallace 0:44 being very persistent with emails. Laura 0:46 Theres also not very many of us Jason Wallace 0:49 there is that when there's actually a website called diatoms.org, that has all the nation's top diatoms scientists linked to it, somehow, it's not that hard to find someone. So before we get into this lovely game about absolutely beautiful, microscopic creatures. Let's start with our fun science facts. So Laura, as our guest, we usually pass the privilege to you to start. Do you have something you'd like to share with our audience? Laura 1:09 Sure. My favorite fact about diatoms is they produce about a fourth of the oxygen we breathe. So they're very important to life on Earth, and we wouldn't survive without them. Brian 1:16 So trees get all the credit, but they're stealing that Jason Wallace 1:19 we talkabout plant blindness, where people just don't look at plants. There's definitely what macroscopic bias, where we just don't think about all the things that aren't within, you know, human size scale. So yeah, trees get all the credit, but all these little microbes are actually doing a whole bunch of the work there. Laura 1:33 Yeah, diatoms, along with other groups of algae, actually produce about half of the oxygen we breathe, so they are as important, if not more important, than land plant, but no one thinks about them, sees them, or really acknowledges them. Brian 1:44 So let me think. Then I'm thinking about this track of carbon dioxide that we've been seeing sort of dip and rise and dip and rise and dip and rise. Now that dip and rise that's from the like Alpine forests in the northern continents, right? But the stable activity that's presumably all the algae in the ocean, right? Or do they also fluctuate on an annual cycle? Jason Wallace 2:04 I'd assume they'd also fluctuate annually, just because of temperature, if nothing else. Laura 2:07 It depends on the environment. So diatoms in the ocean are relatively consistent, but I think it does fluctuate with temperature. I actually don't know too much about marine diatoms, because my expertise lies in benthic freshwater diatoms. Brian 2:19 Benthic freshwater. So that means, like, the things that live in the muck at the bottom of fresh water environments, Laura 2:24 yeah, the brown slime you see when you go to creeks. That's what I love to look at. Brian 2:28 Oh, you're a slimologist. That's awesome. Jason Wallace 2:30 All right, Brian, your turn. What fun fact do you have for us today? Brian 2:33 Well, funny enough, I also brought a diatoms one I was looking for something recently about diatoms in the news. It's a press release out of Stanford, about diatoms remaining active down to negative 15 degrees centigrade, so cold, basically, in solid ice isn't as solid as you'd think. Actually, it can have these little micro fluidic chambers within it, sort of threads of liquid water. And the diatoms were actually not only colonizing these but moving through these chambers. I didn't even know diatoms could move. I guess they have like little actin filaments that they use to move on slime. I want to know more about this, and I'm hoping that Laura can explain it. Laura 3:08 Diatoms are very capable of active movement. Not all of them, though, they have to have a slit in the center of the cell, which is called the raphe and they can secrete mucilage. And they glide along like slugs. Brian 3:18 So you can tell just by looking at them if they're going to be able to be mobile? Laura 3:21 yep, Brian 3:22 Do all the ones with Raphe have mobility? Or do some of them have the Raphe and are not mobile? Laura 3:26 No, all of them have mobility. The raphe can vary in its placement on the cell, whether it's in the center of the cell, along the sides, if it's on one half of the valve. Because diatoms are made in two parts, they're kind of like a box where you have a top half and a bottom half. So when they're dead, they split apart. So you'll see the raphe on one valve and not the other. But they do have their Raphe. Brian 3:43 That's really cool, man. So diatoms are kind of like mimics in D & D. They live in a box. Jason Wallace 3:48 They are a box, a glass box. They make themselves. Brian 3:51 That's okay. These are very cool organisms. Jason Wallace 3:53 They are. So let's go on to this game, then, because this game is a beautiful game about these beautiful creatures. So diatoms is a game by Ludoliminal Games and published by 25th century games. It actually won a 2025 Mensa select award, and I like the tagline on the publisher's website. It is a stunningly beautiful game about making art from algae, which is not something you would think about, but the whole metaphor of this game is about Victorian diatom art, which is this obscure art form, where, back when microscopes, well, microscopes for the masses, were a new fangled thing, and people were trying to sell them. They wanted to sell things that you could look at right away. And so they would sell these little slides you could put under and they had diatom art on them, which is what you're making in this game. We'll talk more about what diatom art is in a little bit for the game itself, its basic stats. It's for one to four players, obligatory single player mode, although I'll say this is one of the few games we've played where I actually have played the single player mode, and I can attest it's actually quite fun. Brian 4:51 Yeah, I was gonna say you actually said you liked it like you enjoyed it. Jason Wallace 4:54 It's very calming. And ages eight plus about 30-45, minutes to play. Suggested retail price is $55 a lot of that is probably going to the very high quality components. So there's very high quality chipboard, most of which has foil embossing on it in some degree, oftentimes, lots. The game is played in two sections. You have your tile placing one where you've got these hexagonal tiles that have colors coming off of them. So every hexagon consists of six triangles joined at the tip. And so those six triangles can be any one of a number of colors. They've got five different ones, red, yellow, green, blue and purple. Some of them are white as wild spots. And it's a typical like color matching game. You have the hexes down on the board, and then you try to place new hexes so that the colors match. That part is fairly straightforward. The thing is based on the colors you make at that intersection. So when you place a hex down next to two other tiles, it forms a point where all three of those tiles touch, and where, therefore there are six triangles around that central point. And the size and distribution of the color patches determines which diatoms you then collect. Metaphorically, this is you like looking at a patch of wa

49 min

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out of 5

4 Ratings

So fun and educational!

09/01/2025

young math momma

I have been listening to Gaming with Science since the beginning, and I love it!! If you enjoy learning and playing games, this is the perfect podcast. I always learn something new. I’ve only played one game they’ve mentioned so far, but I’d love to try some of the other ones they have highlighted. I just really love what I learn from the experts, especially when Jason and Brian get to share their nerd knowledge. They are great!!
These guys rock

05/20/2024

Dtrip96

This is such an interesting concept! Very creative and fun listen, while still being able to be a professional environment for knowledge.

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S3E05.5 - Minecraft (Bonus - Teaching Computers to Game)

S3E05.4 - Go (Bonus - Teaching Computers to Game)

S3E05.3 - Chess (Bonus - Teaching computers to game)

S3E05.2 - Tic-Tac-Toe (Bonus - Teaching computers to game)

S3E05.1 - FloraVista Creator Interview (bonus)

S3E05 - Ark Nova (Zoos)

S3E04.1 - The Mating Game (bonus)

S3E04 - Diatoms (Diatoms)

So fun and educational!

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S3E05.5 - Minecraft (Bonus - Teaching Computers to Game)

S3E05.4 - Go (Bonus - Teaching Computers to Game)

S3E05.3 - Chess (Bonus - Teaching computers to game)

S3E05.2 - Tic-Tac-Toe (Bonus - Teaching computers to game)

S3E05.1 - FloraVista Creator Interview (bonus)

S3E05 - Ark Nova (Zoos)

S3E04.1 - The Mating Game (bonus)

S3E04 - Diatoms (Diatoms)

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