@tiotasram@kolektiva.social2025-08-12 09:01:39
Long post, game design
Crungle is a game designed to be a simple test of general reasoning skills that's difficult to play by rote memory, since there are many possible rule sets, but it should be easy to play if one can understand and extrapolate from rules. The game is not necessarily fair, with the first player often having an advantage or a forced win. The game is entirely deterministic, although a variant determines the rule set randomly.
This is version 0.1, and has not yet been tested at all.
Crungle is a competitive game for two players, each of whom controls a single piece on a 3x3 grid. The cells of the grid are numbered from 1 to 9, starting at the top left and proceeding across each row and then down to the next row, so the top three cells are 1, 2, and 3 from left to right, then the next three are 4, 5, and 6 and the final row is cells 7, 8, and 9.
The two players decide who shall play as purple and who shall play as orange. Purple goes first, starting the rules phase by picking one goal rule from the table of goal rules. Next, orange picks a goal rule. These two goal rules determine the two winning conditions. Then each player, starting with orange, alternate picking a movement rule until four movement rules have been selected. During this process, at most one indirect movement rule may be selected. Finally, purple picks a starting location for orange (1-9), with 5 (the center) not allowed. Then orange picks the starting location for purple, which may not be adjacent to orange's starting position.
Alternatively, the goal rules, movement rules, and starting positions may be determined randomly, or a pre-determined ruleset may be selected.
If the ruleset makes it impossible to win, the players should agree to a draw. Either player could instead "bet" their opponent. If the opponent agrees to the bet, the opponent must demonstrate a series of moves by both players that would result in a win for either player. If they can do this, they win, but if they submit an invalid demonstration or cannot submit a demonstration, the player who "bet" wins.
Now that starting positions, movement rules, and goals have been decided, the play phase proceeds with each player taking a turn, starting with purple, until one player wins by satisfying one of the two goals, or until the players agree to a draw. Note that it's possible for both players to occupy the same space.
During each player's turn, that player identifies one of the four movement rules to use and names the square they move to using that rule, then they move their piece into that square and their turn ends. Neither player may use the same movement rule twice in a row (but it's okay to use the same rule your opponent just did unless another rule disallows that). If the movement rule a player picks moves their opponent's piece, they need to state where their opponent's piece ends up. Pieces that would move off the board instead stay in place; it's okay to select a rule that causes your piece to stay in place because of this rule. However, if a rule says "pick a square" or "move to a square" with some additional criteria, but there are no squares that meet those criteria, then that rule may not be used, and a player who picks that rule must pick a different one instead.
Any player who incorrectly states a destination for either their piece or their opponent's piece, picks an invalid square, or chooses an invalid rule has made a violation, as long as their opponent objects before selecting their next move. A player who makes at least three violations immediately forfeits and their opponent wins by default. However, if a player violates a rule but their opponent does not object before picking their next move, the stated destination(s) of the invalid move still stand, and the violation does not count. If a player objects to a valid move, their objection is ignored, and if they do this at least three times, they forfeit and their opponent wins by default.
Goal rules (each player picks one; either player can win using either chosen rule):
End your turn in the same space as your opponent three turns in a row.
End at least one turn in each of the 9 cells.
End five consecutive turns in the three cells in any single row, ending at least one turn on each of the three.
End five consecutive turns in the three cells in any single column, ending at least one turn on each of the three.
Within the span of 8 consecutive turns, end at least one turn in each of cells 1, 3, 7, and 9 (the four corners of the grid).
Within the span of 8 consecutive turns at least one turn in each of cells 2, 4, 6, and 8 (the central cells on each side).
Within the span of 8 consecutive turns, end at least one turn in the cell directly above your opponent, and end at least one turn in the cell directly below your opponent (in either order).
Within the span of 8 consecutive turns at least one turn in the cell directly to the left of your opponent, and end at least one turn in the cell directly to the right of your opponent (in either order).
End 12 turns in a row without ending any of them in cell 5.
End 8 turns in a row in 8 different cells.
Movement rules (each player picks two; either player may move using any of the four):
Move to any cell on the board that's diagonally adjacent to your current position.
Move to any cell on the board that's orthogonally adjacent to your current position.
Move up one cell. Also move your opponent up one cell.
Move down one cell. Also move your opponent down one cell.
Move left one cell. Also move your opponent left one cell.
Move right one cell. Also move your opponent right one cell.
Move up one cell. Move your opponent down one cell.
Move down one cell. Move your opponent up one cell.
Move left one cell. Move your opponent right one cell.
Move right one cell. Move your opponent left one cell.
Move any pieces that aren't in square 5 clockwise around the edge of the board 1 step (for example, from 1 to 2 or 3 to 6 or 9 to 8).
Move any pieces that aren't in square 5 counter-clockwise around the edge of the board 1 step (for example, from 1 to 4 or 6 to 3 or 7 to 8).
Move to any square reachable from your current position by a knight's move in chess (in other words, a square that's in an adjacent column and two rows up or down, or that's in an adjacent row and two columns left or right).
Stay in the same place.
Swap places with your opponent's piece.
Move back to the position that you started at on your previous turn.
If you are on an odd-numbered square, move to any other odd-numbered square. Otherwise, move to any even-numbered square.
Move to any square in the same column as your current position.
Move to any square in the same row as your current position.
Move to any square in the same column as your opponent's position.
Move to any square in the same row as your opponent's position.
Pick a square that's neither in the same row as your piece nor in the same row as your opponent's piece. Move to that square.
Pick a square that's neither in the same column as your piece nor in the same column as your opponent's piece. Move to that square.
Move to one of the squares orthogonally adjacent to your opponent's piece.
Move to one of the squares diagonally adjacent to your opponent's piece.
Move to the square opposite your current position across the middle square, or stay in place if you're in the middle square.
Pick any square that's closer to your opponent's piece than the square you're in now, measured using straight-line distance between square centers (this includes the square your opponent is in). Move to that square.
Pick any square that's further from your opponent's piece than the square you're in now, measured using straight-line distance between square centers. Move to that square.
If you are on a corner square (1, 3, 7, or 9) move to any other corner square. Otherwise, move to square 5.
If you are on an edge square (2, 4, 6, or 8) move to any other edge square. Otherwise, move to square 5.
Indirect movement rules (may be chosen instead of a direct movement rule; at most one per game):
Move using one of the other three movement rules selected in your game, and in addition, your opponent may not use that rule on their next turn (nor may they select it via an indirect rule like this one).
Select two of the other three movement rules, declare them, and then move as if you had used one and then the other, applying any additional effects of both rules in order.
Move using one of the other three movement rules selected in your game, but if the move would cause your piece to move off the board, instead of staying in place move to square 5 (in the middle).
Pick one of the other three movement rules selected in your game and apply it, but move your opponent's piece instead of your own piece. If that movement rule says to move "your opponent's piece," instead apply that movement to your own piece. References to "your position" and "your opponent's position" are swapped when applying the chosen rule, as are references to "your turn" and "your opponent's turn" and do on.
#Game #GameDesign
@cyrevolt@mastodon.social2025-09-13 16:49:56
Here is my writeup on the adventure I faced with Go's use of syscalls recently:
https://gist.github.com/orangecms/7f985ef19398544114599430b79868f7
@arXiv_csIR_bot@mastoxiv.page2025-08-13 08:53:22
Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs
Pierre Maillot (WIMMICS), Catherine Faron (UniCA, I3S, WIMMICS), Fabien Gandon (WIMMICS), Franck Michel (Laboratoire I3S - SPARKS, WIMMICS), Pierre Monnin (WIMMICS)
https://arxiv.org/abs/2508.08713
@burger_jaap@mastodon.social2025-09-11 13:52:11
In addition to the other EV reports published by BEUC, the European Consumer Association, today, there is also this one on improving the public EV charging experience. It addresses price transparency, competition, unfair pricing practices, and the need to accelerate dynamic pricing and smart charging on public charging points.
@cdarwin@c.im2025-08-11 01:08:16
As we turned into Lone Pine to begin the half-marathon ascent of Mount Whitney,
I got the sense that Frederick had been distilled into a person whose focus was so singular that she only needed someone to show her direction.
That someone was Boyd.
Frederick asked him to pace the miles into the base of the climb,
and it was then that Boyd told her,
finally, that she was on pace to break the age-group record.
This was the final arrow in his quiver.
…
@laurentperrinet@neuromatch.social2025-08-11 12:06:30
This year at #CCN2025 we will be showcasing our research on the classification of Mental Workload 🥵 Spatial Effects using Riemannian Manifold.
📅 When: Wednesday, August 13, 1:00 – 4:00 pm
📍 Where: CCN 2025 Conference Venue, de Brug & E-Hall
📋 What: Poster B152
- It leverages advanced mathematical techniques to better understand and classify mental workloads, offerin…
@tiotasram@kolektiva.social2025-08-04 15:49:00
Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2
@arXiv_csDC_bot@mastoxiv.page2025-08-11 07:34:29
Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Brandon Baker, Elliott Brossard, Chenwei Xie, Zihao Ye, Deen Liu, Yijun Xie, Arthur Zwiegincew, Nitya Kumar Sharma, Gaurav Jain, Eugene Retunsky, Mike Halcrow, Derek Denny-Brown, Istvan Cseri, Tyler Akidau, Yuxiong He
https://arxiv.org/abs/2508.05904…
@arXiv_csIR_bot@mastoxiv.page2025-07-10 08:50:11
GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
Zhen Yang, Haitao Lin, Jiawei xue, Ziji Zhang
https://arxiv.org/abs/2507.06507
@tiotasram@kolektiva.social2025-07-28 13:06:20
How popular media gets love wrong
Now a bit of background about why I have this "engineered" model of love:
First, I'm a white straight cis man. I've got a few traits that might work against my relationship chances (e.g., neurodivergence; I generally fit pretty well into the "weird geek" stereotype), but as I was recently reminded, it's possible my experience derives more from luck than other factors, and since things are tilted more in my favor than most people on the planet, my advice could be worse than useless if it leads people towards strategies that would only have worked for someone like me. I don't *think* that's the case, but it's worth mentioning explicitly.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
I'm lucky in that I had some mixed-gender social circles already like intramural soccer and a graduate-student housing potluck. Graduate school makes a *lot* more of these social spaces accessible, so I recognize that those not in school of some sort have a harder time of things, especially if like me they don't feel like they fit in in typical adult social spaces like bars.
However, at one point I just decided that my desire for a relationship would need action on my part and so I'd try to build a relationship and see what happened. I worked up my courage and asked one of the people in my potluck if she'd like to go for a hike (pretty much clearly a date but not explicitly one; in retrospect not the best first-date modality in a lot of ways, but it made a little more sense in our setting where we could go for a hike from our front door). To emphasize this point: I was not in love with (or even infatuated with) my now-wife at that point. I made a decision to be open to building a relationship, but didn't follow the typical romance story formula beyond that. Now of course, in real life as opposed to popular media, this isn't anything special. People ask each other out all the time just because they're lonely, and some of those relationships turn out fine (although many do not).
I was lucky in that some aspects of who I am and what I do happened to be naturally comforting to my wife (natural advantage in the "appeal" model of love) but of course there are some aspects of me that annoy my wife, and we negotiate that. In the other direction, there's some things I instantly liked about my wife, and other things that still annoy me. We've figured out how to accept a little, change a little, and overall be happy with each other (though we do still have arguments; it's not like the operation/construction/maintenance of the "love mechanism" is always perfectly smooth). In particular though, I approached the relationship with the attitude of "I want to try to build a relationship with this person," at first just because of my own desires for *any* relationship, and then gradually more and more through my desire to build *this specific* relationship as I enjoyed the rewards of companionship.
So for example, while I think my wife is objectively beautiful, she's also *subjectively* very beautiful *to me* because having decided to build a relationship with her, I actively tried to see her as beautiful, rather than trying to judge whether I wanted a relationship with her based on her beauty. In other words, our relationship is more causative of her beauty-to-me than her beauty-to-me is causative of our relationship. This is the biggest way I think the "engineered" model of love differs from the "fire" and "appeal" models: you can just decide to build love independent of factors we typically think of as engendering love (NOT independent of your partner's willingness to participate, of course), and then all of those things like "thinking your partner is beautiful" can be a result of the relationship you're building. For sure those factors might affect who is willing to try building a relationship with you in the first place, but if more people were willing to jump into relationship building (not necessarily with full commitment from the start) without worrying about those other factors, they might find that those factors can come out of the relationship instead of being prerequisites for it. I think this is the biggest failure of the "appeal" model in particular: yes you *do* need to do things that appeal to your partner, but it's not just "make myself lovable" it's also: is your partner putting in the effort to see the ways that you are beautiful/lovable/etc., or are they just expecting you to become exactly some perfect person they've imagined (and/or been told to desire by society)? The former is perfectly possible, and no less satisfying than the latter.
To cut off my rambling a bit here, I'll just add that in our progress from dating through marriage through staying-married, my wife and I have both talked at times explicitly about commitment, and especially when deciding to get married, I told her that I knew I couldn't live up to the perfect model of a husband that I'd want to be, but that if she wanted to deepen our commitment, I was happy to do that, and so we did. I also rearranged my priorities at that point, deciding that I knew I wanted to prioritize this relationship above things like my career or my research interests, and while I've not always been perfect at that in my little decisions, I've been good at holding to that in my big decisions at least. In the end, *once we had built a somewhat-committed relationship*, we had something that we both recognized was worth more than most other things in life, and that let us commit even more, thus getting even more out of it in the long term. Obviously you can't start the first date with an expectation of life-long commitment, and you need to synchronize your increasing commitment to a relationship so that it doesn't become lopsided, which is hard. But if you take the commitment as an active decision and as the *precursor* to things like infatuation, attraction, etc., you can build up to something that's incredibly strong and rewarding.
I'll follow this up with one more post trying to distill some advice from my ramblings.
#relationships #love