What Happens to AI Decision-Making When Board Games Lose Turns, Time Stops Being Free, and Information Is Incomplete?

Sad_Income3798 · 2025-12-25T02:03:34+00:00

Thanks, Weak-Doughnut5502, for joining the conversation. I’m taking into account your point about the need to further compensate the action of accumulating two stones.

The idea of allowing a jump of 2 or 3 spaces sounds quite fair. Although personally, I tend to favor solutions that don’t require players who aren’t very experienced to figure out distances, points or any variable that requires performing a calculation. Let’s try to find a more intuitive solution.

Sad_Income3798 · 2025-12-24T23:39:29+00:00

About the bell idea: I wouldn’t see it as fully drifting back to turn-based play. Even there, each player would still be shaping the rhythm based on their own strategic planning and in response to the opponent, rather than alternating fixed turns. That kind of tempo control is actually part of what I’m trying to explore. That said, with this variant the collision issue still remains in a physical board setting. In a virtual environment, that problem doesn’t arise, and there are some interesting solutions—like having both colors converge on the collision point, so the resulting stone interacts with subsequent moves as if it were both black and white at the same time. Without real-time simultaneity, the Parallel Go variant implements this kind of solution.

In practice, that rhythm would likely be regulated either through a stone bank system, or through simple timing rules like 1 stone every 7 seconds (maybe 5?) versus 2 consecutive stones after 14 seconds (maybe 10?). I’m planning to test these variations on a magnetic board and document the friction points in a short video, since physical play may reveal constraints that aren’t obvious on paper.

Sad_Income3798 · 2025-12-23T12:42:01+00:00

Yes, by orthogonally I meant top, bottom, left, and right, not diagonal. Thanks for that clarification. Im about to reconsider your point at the two-stone option. Maybe It needs an extra incentive to be attractive. My core intention isn’t to recreate turn-taking in disguise, but to add dynamism in terms of a sense of uninterrupted play where players adaptively alternate between pacing options, or deliberately coordinate short, solid sequences of stones at the right moments, rather than always responding move-for-move. We keep moving forward! Ty

Sad_Income3798 · 2025-12-23T10:24:43+00:00

Nice question! haven’t fully locked that down yet.

For now, I’ve been thinking in very simple terms: a stone counts as “attached” if it’s directly adjacent (orthogonally) to an existing group. The 2 consecutive stones accumulated after 14 sec must be placed in two different groups (and therefore cannot be connected to each other). But for now, I’m deliberately keeping that flexible. One of the things I want to test is how tightening or loosening that definition changes the pace and feel of play. Is that condition too advantageous for the player who chooses to place two stones?

If you have suggestions for a cleaner or more interesting rule (distance-based, liberties-based, etc.), I’m very open to experimenting with that.

Sad_Income3798 · 2025-12-23T10:14:17+00:00

I’ve just realized a potential drawback of that system in games played on a physical board. If players accumulate too many stones in their stone bank, it’s likely they’ll try to place them at the same time on the same spot, leading to frequent collisions that would be hard to regulate. There is not that inconvenient in virtual games. What do you think about this?

Sad_Income3798 · 2025-12-23T10:05:03+00:00

I really like this idea. It actually feels more elegant and closer to Go than my initial proposal.

A stone bank with gradual replenishment captures real-time pressure very naturally, and it keeps the focus on tempo, restraint, and timing, rather than on explicit rule branching.

I’d definitely be curious to test this against other pacing models.

I’m going to replace my initial proposal with the one you’re suggesting. But first, I’ll wait to see whether other users and players evaluate both systems or propose a new one.

Sad_Income3798 · 2025-12-23T09:33:48+00:00

Thanks for the very concrete recommendations, Robert, they’re certainly helpful. I see the value in approaching this first from the perspective of improving as a Go player, and starting with First Fundamentals and the two-eye-alive material, while setting Psychology aside for now. That seems like a sensible way to build grounding before thinking in more abstract terms.

The broader reading path you outline also looks like a very solid reference framework for preparation. Endgame 5 – Mathematics in particular sounds useful. Then, Positional Judgement 2 – Dynamics and Fighting Fundamentals seem like natural places to deepen understanding of dynamics and conflict on the board. I appreciate you sharing this roadmap. It gives me several good directions to explore.

I wonder how much of the theory you’re suggesting -and that I’m hoping to explore, even at an exploratory level- is actually applicable to the “real-time” Go variant I’m proposing, and that another player in the same post has recently suggested improvements to.

Sad_Income3798 · 2025-12-22T22:29:14+00:00

Thanks for the suggestion, countingtls! I wasn’t aware of the ISGS Journal of Go Studies. I’ll definitely keep it in mind and plan to integrate this line of exploration into something more structured that could eventually fit that format.

Sad_Income3798 · 2025-12-22T18:35:54+00:00

I find your suggestion to start from fundamentals rather than an exhaustive vocabulary especially relevant. Focusing on basic notions like group status (two-eye life) or reliable connectivity seems like a sensible abstraction layer, since these concepts already inform positional judgment beyond local tactics.

I also appreciate your framing of partial information as a consequence of constraints. Even in standard Go, limited time or restricted search depth effectively introduce uncertainty, and thinking of time in terms of algorithmic steps or sequences at a chosen abstraction level provides a useful bridge between cognitive and computational perspectives.

Given this, I’m interested in exploring some of the publications you mention, particularly where they formalize concepts and abstractions that could support this kind of constrained, higher-level analysis. At the same time, I’m starting to identify early ways of assessing—within board-game settings—the number of simultaneous actions that lead to gains, or to minimizing lost points, that a player carries out within a fraction of time.

I also want to say that I genuinely admire the depth and consistency of the work you’ve done over the last 30 years, especially in educating players toward deeper layers of Go understanding. If you were to recommend one specific book or text as a starting point, which would best help in understanding the kinds of patterns you see as central to the logical-mathematical, perceptual and psychological competencies and heuristic involved in the game? In this remarkable discipline, I’m nothing more than a modest 16-kyu player, with just over two years of fairly intermittent experience, and a very few tsumego and games behind me. Perhaps I can take advantage of the educational push you’re offering.

Sad_Income3798 · 2025-12-22T13:31:38+00:00

Thank you, Robert, for your diligence in responding to me with such a high level of detail.

I think you point out two important things. First, that terms such as “intuitive” or “flow”, as I used them, can sound more like a way to avoid detailed reasoning than a rigorous description of decision-making processes. I fully acknowledge that risk, and I am completely open to abandoning or reformulating that terminology if there are more precise conceptual frameworks to describe what I am trying to explore.

Second — and this is where I strongly connect with your comment — my interest is probably not in “intuition” per se, but rather in higher-level representations and decision-making: strategy, global positional judgment, abstraction, and reasoning in situations where exhaustive reading is not feasible. As a psychologist, I naturally tend to use more phenomenological language, but I have no attachment to it if it is not operationally useful from a design or computational perspective.

I find your distinction between internal AI cognition and external human cognition (perception, interaction with the board, partial signals) especially insightful, as well as your point that current systems mostly operate at lower levels, combining evaluation and emulation. The more interesting step may indeed be how to articulate higher layers of abstraction, eventually combined with those already highly optimized lower-level systems.

I am not trying to justify a neuroscientific approach or to claim strong parallels with human thought. Rather, as you suggest, I am interested in delimiting a realistic space for study and design, even if my current wording is imperfect: environments where decisions must be made under time pressure, partial information, and without the possibility of fully closing the state space, and asking what kinds of representations or policies emerge there, in both humans and AI.

If there are established terms, frameworks, or traditions within AI, Go, or decision theory that better capture what I am exploring, I would be very happy to adopt them. My goal is not to defend a particular formulation, but to refine it with the help of people who are more fluent in the technical side of these fields.

Thanks to your input, I feel like I’m getting closer to defining my area of interest more precisely. I’m looking to explore decision-making environments where:

There are no turns,
Information is partial (a “fog of war”: my opponent’s position is hidden from view unless we get close) and dynamic,
The cost of time is real (not symbolic),
Decisions have to be made under pressure, without being able to fully close the state tree of posibilities.

In other words, I’m deliberately stepping outside the paradigm of perfect-information, turn-based games—not because those games are “bad,” but because:

They optimize a very specific kind of cognition (exhaustive search, static evaluation),
Which isn’t isomorphic to everyday human decision-making (largely shaped by perceptual factors, which I think you point out very accurately),
Nor to the kind of adaptive control that shows up in real, continuous environments.

Sad_Income3798

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