GameGrammar - The Theory of Generative Board Game Design

A poet needs grammar. A game designer needs structure. This article lays out the design theory behind GameGrammar, a theory born from one practical question: Can AI help create playable board games? The answer turned out to require more than clever prompting. It required a shared vocabulary for what games are, a way to generate what games could be, and a collaborative process for refining what games should become. What follows is that theory, and a direct answer to two questions every designer asks: Can AI really understand “fun”? And can AI be genuinely creative?

Figure. Structure meets imagination. The left half shows the blueprint, the right half shows the finished piece. GameGrammar bridges both worlds.

GameGrammar did not begin as a theory. It began as a practical experiment at Dynamind Research [7]: type a theme into a box, let six AI agents build a complete board game, and see what comes out. What came out, after months of iteration, was not just a tool but a set of ideas about how design works, why AI can be a trustworthy creative partner, and what that means for human designers.

In a previous article, we showed what GameGrammar produces: twelve words in, a complete game out in 73 seconds. This article goes deeper. It explains the why behind the what, the design thinking that makes human-AI game design not just possible, but genuinely new.

Where GameGrammar Fits: The Board Game Production Pipeline

Before we explore the ideas, it helps to understand where GameGrammar sits in a game designer’s workflow. The journey from idea to published box on a shelf is a nine-stage pipeline, and most people only see the last few stages [7]:

The graveyard is in Stages 1 and 2. This is where designers spend the most time, where most “cool mechanics” die (and should), and where motivation quietly erodes. The blank page is the first enemy. Before you can even begin the playtesting gauntlet, you need a concept worth testing: not just a theme, but a coherent combination of mechanics, components, player dynamics, and victory conditions.

Figure. GameGrammar sits between Concept and Testing, providing rapid variant generation, automated stress-testing, and rule structure scaffolding. It helps designers move faster through the friction-heavy early pipeline, but does not design the game for you.

GameGrammar lives at Stages 1 and 2. It is a design workbench for the earliest and most uncertain phases of game creation:

• Stage 1: Generate structured first drafts from a theme and constraints. Beat blank-page paralysis. Explore mechanism combinations drawn from real published games.
• Stage 2: Iterate rapidly with AI-assisted consistency checking, balance analysis, section-by-section rewriting, and plain-language editing. Catch issues that would normally take weeks of playtesting to surface.

GameGrammar does not touch Stages 3 through 9. It will not lock your design, pitch to publishers, produce art, or manage manufacturing. It sits precisely where you need the most help and where AI can do the most good: turning a theme into a testable design, and helping you refine that design through structured iteration.

The positioning matters. GameGrammar is a design accelerator that helps you move faster through the early pipeline. It is not a replacement for your craft. You remain the designer. The AI is your instrument.

The Core Idea

Game design is a structured creative act. It can be broken into a shared vocabulary of game elements, powered by AI generation, and refined through back-and-forth collaboration between human and machine. The result is something neither purely human nor purely AI, but a co-designed partnership that plays to the strengths of both.

This idea rests on three observations:

1. Games share a common structure. Beneath the surface diversity of tabletop games lies a shared language: mechanisms, components, player dynamics, turn structures, scoring systems. That language can be captured in useful detail.

2. Structure makes generation possible. When you encode that language as a detailed template, AI can generate within it. The template becomes a grammar that enables valid, coherent, novel designs, not by limiting creativity, but by giving it a vocabulary to be creative within.

3. Design is iterative, not instantaneous. No generated design is finished. The real work happens in the refinement loop: spotting contradictions, updating connected sections, rewriting what has gone stale, translating your intent into concrete changes. This loop needs your judgment, guided by AI analysis.

Why Constraints Enable Creativity

The theory starts with a paradox familiar to many designers. Without structure, there is no coherent creation.

A poet needs grammar. A game designer needs structure.

If you ask a general-purpose AI to “design a board game,” you get fluent, confident text that falls apart the moment you try to prototype it. No concrete card counts. No defined end condition. No actual scoring math. The structured vocabulary that GameGrammar uses is what makes valid generation possible. The constraints do not limit creativity. They are the reason creativity can happen.

Think of it this way: most game design vocabularies describe what games are. GameGrammar’s vocabulary creates what games could be. By capturing game design knowledge as a detailed template, with required fields, defined mechanism categories, and relationships between parts, we turn a passive description into a generative starting point. The template says: every game must have a goal, an end condition, mechanisms that create player choices, and components that bring those mechanisms to life. Within those boundaries, infinite games are possible. Without them, no valid game comes out.

The Philosophical Thread

There is a philosophical idea running beneath all of this, drawn from Alfred North Whitehead’s process philosophy [1][2], which we explored in depth in a previous article. The key distinction is between abstract patterns (like worker placement, deck building, or area control as general concepts) and concrete games (where those patterns become specific cards, tokens, rules, and boards).

Figure. Abstract patterns become concrete games. The patterns constrain but do not determine. The creativity is real, but it is structured creativity.

GameGrammar’s job is to move from abstract patterns to concrete games. The vocabulary provides the patterns; AI generation produces the specific instances. Each generated game is genuinely new, a fresh combination of familiar building blocks arranged in a way that did not exist before.

Bridging Structure and Imagination

Structure and imagination have opposite strengths:

Figure. GameGrammar brings together what structure and imagination each do best. The result is designs that are both novel and valid, something that could not exist without both halves working together.

GameGrammar combines both. The structured vocabulary provides the constraints; the AI provides the creative substance. The result is designs that could not come from either half alone.

The Six-Agent Pipeline

Generation is not a single AI prompt. It is a team of six specialized agents, each focused on one area of game design [7]:

Figure. Six specialized agents working in sequence. Each agent sees the complete output of all previous agents, building on their work like a relay team of expert consultants.

The Mechanics Architect picks mechanisms from a curated library of 35+ game mechanisms drawn from Engelstein and Shalev’s taxonomy [3]. The Theme Weaver dresses the mechanics in your theme. The Component Designer specifies every physical piece with concrete counts. The Detail Expander writes out turn phases, card examples, and scoring rules. The Balance Critic flags potential issues in the numbers. The Design Evaluator scores six dimensions of design quality, maps the emotional arc across game phases, and measures how original your mechanism combination is. You get a creative profile, not just a single rating.

Grounded in Real Games

Generated designs are not invented from thin air. GameGrammar draws on a reference library of 2000+ existing games from BoardGameGeek [6]. When generating a worker placement game, the system looks at successful worker placement designs, their component counts, player counts, mechanism pairings, and balance approaches, and uses them as guideposts for your new design. This keeps generated games grounded in patterns that have proven themselves in published, well-regarded games.

The same reference library serves a second purpose: measuring originality. After generation, your design’s mechanism combination is compared against the full collection of existing games. This produces a novelty score. “Your combination of worker placement plus real-time resolution appears in only 3% of reference games.” It is consistent, explainable, and genuinely rewarding when you discover you are exploring uncharted design territory.

Three Layers: Game, Depth, and Process

Your game design in GameGrammar lives on three separate layers, and keeping them separate turned out to be one of the more useful design decisions we made.

Figure. Three layers keep generation clean, editing informed, and your design’s evolution visible. Most design tools mix the game with the process of making it. GameGrammar separates them.

Layer 1, Your Game, captures the building blocks of any tabletop game: mechanisms, components, turn structure, player dynamics, scoring. This is the creative vocabulary itself.

Layer 2, The Details, adds concrete specifics: what actually happens during each turn phase, example card text, scoring formulas with real numbers. This is the difference between “this game has a drafting phase” and “in the drafting phase, each player picks one card from a shared pool of 5, resolving simultaneously.”

Layer 3, The Design Process, tracks everything about how you are working on the game, not the game itself: which sections need updating, consistency issues, AI suggestions you have saved, your version history. None of this is about your game. It is about the process of making your game better.

The House Metaphor

Think of it like building a house:

Figure. Layer 1 is the blueprints. Layer 2 is the interior design. Layer 3 is the contractor’s clipboard.

Layer 1 is the blueprints: foundation and load-bearing walls. You cannot live in them, but you cannot build without them. Layer 2 is the interior design: furniture and paint. You can swap the couch without tearing down walls. Layer 3 is the contractor’s clipboard: the punch list, renovation history, and building permits. A record of the work being done, not the building itself.

This metaphor also explains what happens when you make a big change. If you move a load-bearing wall (change a core mechanism), the furniture layout becomes outdated: the sofa is now halfway through a wall. GameGrammar tracks these connections automatically.

Figure. When you change the blueprints, the interior needs reviewing. The contractor’s clipboard is never outdated; it just records that renovation is underway.

The Co-Design Partnership: Generation Is Not Design

Here is the most important insight behind GameGrammar: generating a game is not the same as designing one. A generated first draft, no matter how good, is a starting point. The real design happens in the back-and-forth that follows.

Figure. The co-design loop: you edit, connected sections update, the AI spots emerging issues, you choose how to refine, changes get versioned. Generation sits at the center, but design lives in the cycle around it.

After your first draft is generated, the design enters a continuous refinement cycle. You make changes. Connected sections get flagged for review. The AI spots contradictions and emerging issues. You choose a path forward. The AI proposes solutions with clear reasoning. You preview everything before it touches your design. You accept, modify, or reject. Your evaluation scores update, showing which dimensions improved and which declined. The cycle continues.

One rule is absolute: the AI never changes your design without your permission. Every modification follows a preview-before-apply pattern. The AI shows you what it wants to change and why. You decide. This is not just a convenience feature. It is a philosophical commitment to who owns the design.

AI proposes. You decide.

The AI does not understand why you chose a particular mechanism or what feeling you are trying to create at the table. It can spot that your cooperative game has conflicting competitive elements. But only you know whether that tension is a bug or the whole point.

Five Ways the AI Can Help

The partnership offers five distinct modes of assistance, each suited to different moments in your design process:

Figure. From precise fixes to open-ended creative guidance. You choose which mode fits your current need.

Re-evaluation is especially powerful because it closes the feedback loop. After making changes through any of the other modes, you can re-score your design and see what moved. Dimensions that improved flash green; those that declined flash amber. This turns the cycle from “edit and hope” into “edit, measure, and iterate with direction.”

Your Design Workbench: Four Stages of Creation

A game design in GameGrammar moves through four recognizable stages, each with its own rhythm and its own set of AI tools:

Figure. Four stages from first draft to final polish. The workbench is fluid: you move between stages as the design evolves.

Stage 1, Genesis. You provide a theme, some constraints (player count, complexity, play time), and optionally pick a few mechanisms you want to include. The six-agent pipeline generates your complete first draft. You receive a full game design with a creative profile: six scored design dimensions displayed as a radar chart, an emotional arc across game phases, a theme cohesion score, and an originality percentile.

Stage 2, Shaping. You start editing. Swapping mechanisms, adjusting numbers, reworking the turn structure. Each change flags connected sections that may need updating. The AI helps with change tracking, section rewrites, and consistency checking.

Stage 3, Refinement. The big decisions are made. Now you fine-tune: adjusting balance, clarifying card effects, tightening scoring. AI assistance shifts to targeted fixes, plain-language editing (“make the hand limit 7 instead of 5”), and proactive suggestions for improvements you might not have considered.

Stage 4, Polish. Your game works. Now you focus on coherence, theme integration, and overall quality. The Creative Coach dashboard gives you the complete picture at a glance: your radar chart, originality score, theme cohesion, and emotional arc.

These stages are not a rigid sequence. You might be fine-tuning in Stage 3 when a mechanism swap sends you back to Stage 2. You might spot a weak dimension on your radar chart in Stage 4 that sparks a whole new direction. The workbench moves with you.

Design Health: From Bug Checker to Creative Coach

Early versions of GameGrammar’s health system only measured what was wrong with a design: contradictions, outdated sections, unresolved suggestions. Those checks are useful, but they answer the wrong question. A game with zero contradictions is functional. It is not necessarily good.

Figure. The Creative Coach dashboard for Stellar Rivals. The radar chart shows six design dimensions at a glance (Strategy 8, Tension 7, Agency 8, Replay 8, Social 6, Elegance 7). To the right: Fun Rating (8.0/10), Novelty (100%), and Theme Cohesion (9/10). Below: the Engagement Arc across game phases, with a suggestion to add peaks. The AI Assist toolbar offers one-click access to Re-Evaluate, Check Consistency, AI Edit, and AI Suggestions.

The deeper insight: as a designer, you need to know not just whether the blueprint is structurally sound, but whether the game has soul. We needed to move from “nothing is broken” to “here is what makes this design special.”

GameGrammar now tracks your design on two levels:

Level 1, Structural Soundness (the floor, not the ceiling): consistency score, outdated section count, unresolved suggestions. Necessary housekeeping, but not what gets you excited.

Level 2, Creative Vitality (what you actually care about):

The six dimensions turn the vague question “is this fun?” into actionable creative directions. Instead of a single number, you see where your design is strong and where it could grow. Strategic depth might be a 9 while social interaction sits at a 4, and that is perfectly fine if you are designing a solo puzzle game.

The Originality Score gives you a creative thrill by showing how your mechanism combination compares to published games. A score of 87 means “only 13% of existing games share this combination.” It rewards you for venturing into unexplored design space.

The Engagement Curve maps the emotional arc of your game across its phases. A flat line suggests a one-note experience; peaks and valleys suggest drama. A horror game should spike. A meditative engine-builder should flow smoothly. The curve makes your intended emotional experience visible and measurable.

These metrics are not report cards. A low social interaction score is not a failing grade. Your game might not need social interaction. The dashboard answers “What makes this design special?” not “What is wrong?”

Can AI Understand “Fun”?

This is the question every designer asks, and it deserves a straight answer.

Figure. AI has never felt the tension of a close game. But it can recognize the design choices that create that tension.

The short answer: no, AI cannot experience fun. It has never felt the excitement of a close finish, the satisfaction of a clever combo, or the social electricity of pulling off a bluff. It has no taste. It has no feelings.

But here is the thing: you do not need to feel fun to recognize the design patterns that produce it.

Fun in board games is not random magic. It comes from choices you make as a designer:

Figure. The AI spots the patterns; the players bring the experience.

A bridge engineer does not need to “feel beauty” to know the math that makes a bridge elegant. A music teacher can explain why a chord progression creates tension and release without weeping every time they hear it. Analysis and experience operate on different levels.

There are parts of fun that stay irreducibly personal: the chemistry of your play group, the cultural resonance of a theme, the satisfying weight of wooden tokens in your hand, the occasional magic that defies explanation. AI cannot evaluate those. This is exactly why the ground rule exists: AI proposes, you decide. The AI provides structural analysis. You decide whether it matters for this game and this group of players.

The useful question is not “Can AI understand fun?” but “Can AI spot the design patterns that tend to produce fun, so you can focus your energy on the parts only a human designer can provide?” The answer to the first is no. The answer to the second is yes, and that is the only question that matters for a design tool.

Is AI “Creative”?

A related question: Can AI be genuinely creative, or does it just shuffle existing ideas around? This question is about generation, not evaluation, and it has a surprisingly good answer.

Figure. Creativity is not making something from nothing. It is combining known ingredients in ways no one has combined them before.

The philosopher Whitehead defined creativity not as making something from nothing, but as the novel combination of existing elements [1]. Every creative act borrows from what came before but achieves something new in how it brings those pieces together. A poet inherits language, meter, and tradition; the poem is new. An architect inherits materials, engineering, and precedent; the building is new. Creativity is not the absence of inheritance. It is the fresh synthesis of what you have inherited.

This is exactly what GameGrammar does. The vocabulary provides the inherited building blocks of game design: worker placement, deck building, area control, set collection, hidden bidding. These patterns have been discovered, tested, and refined across thousands of published games. The AI generates a specific new game that combines those building blocks in a configuration that did not previously exist.

The criticism that “AI just recombines” is fair, but consider: much of human game design works the same way. Catan combined resource trading with hex grids and dice production: three familiar patterns, one landmark game. Dominion combined deck building with card drafting. Root combined area control with asymmetric player powers. A great deal of creativity in game design comes from the novel recombination of known mechanisms [3]. The difference is that human designers bring taste, experience, and intention to the process. GameGrammar makes the recombination step visible and fast, so you can spend more time on the parts that require your judgment.

Figure. AI has no “why.” You provide the vision; the AI provides the structural possibilities.

What AI genuinely lacks is intention. When you say “I want to create the feeling of surviving in a harsh wilderness,” that vision, that lived experience compressed into a creative direction, is yours alone. The AI has no memories, no desires, no reasons to create this game rather than that one. This is why the process separates your vision (human) from generation (AI) from refinement (collaborative). You provide the “why.” The AI provides the “how.” The design that emerges from working together is genuinely new.

A New Way of Working

Figure. Design as structured creative partnership. Not a blank page, and not one-click magic either.

GameGrammar suggests that game design works best as a three-part activity:

1. Your Vision: choosing the experience you want to create, the theme, player count, complexity, who will play it. This is entirely yours.
2. AI Generation: translating that vision into a structured first draft, grounded in real game design knowledge. This is where AI does its best work.
3. Collaborative Refinement: shaping the generated design through your judgment, assisted by AI analysis. This is where the partnership shines.

This is different from both traditional design (entirely solo, often unstructured, reliant on personal experience) and naive AI generation (typing “make me a board game” and hoping for the best). It positions design as a creative partnership where your vision and AI capability each contribute what they do best.

Figure. The blank page becomes a structured first draft. Manual tracking becomes automatic change detection. Gut feeling becomes measurable creative vitality.

GameGrammar does not claim that AI can replace you. The theory puts the designer firmly in the author’s chair and the AI in the assistant’s seat. The AI can spot that your cooperative game has contradictory competitive elements. It cannot know whether you put that tension there on purpose. The value is in making the structural side of design faster and more visible, so you can pour your energy into the creative decisions that only a human designer can make.

Concluding Remarks

GameGrammar’s design theory can be stated simply:

Games have shared structure. That structure makes generation possible. But generation is not design. Design is the back-and-forth refinement of a generated starting point, where AI proposes and the designer decides.

No single piece of this is revolutionary on its own. Structured game vocabularies exist. AI generation exists. Iterative design is as old as design itself. The contribution is the synthesis: a unified workbench where structured vocabulary, AI generation, change tracking, consistency checking, proactive suggestions, six-dimensional evaluation, and creative vitality metrics all work together as one coherent system.

The three-layer model keeps your game separate from the process of making it. The preview-before-apply rule keeps you in control. The Creative Coach dashboard answers not “what is broken?” but “what makes this design special?” And the four-stage workbench, from genesis through shaping, refinement, and polish, gives you a natural rhythm for the work.

For the design community, this offers a new way of working: not AI replacing designers, not designers ignoring AI, but a structured partnership where each contributes what they do best. The grammar constrains. The AI creates within those constraints. You decide what the game should be.

The grammar does not write the poem. But without grammar, there is no poem to write.

GameGrammar is available in public beta at gamegrammar.dynamindresearch.com. Try it. Generate a game from your favorite theme. Study the radar chart. Make some changes and hit Re-Evaluate. Watch the scores move. Then decide for yourself whether this partnership is worth having.

References

[1] Alfred North Whitehead. Process and Reality. Free Press, 1929/1978.

• The philosophical foundation for how abstract design patterns become concrete games

[2] Benny Cheung. Process Philosophy for AI Agent Design. bennycheung.github.io, Jan 2026.

• A deeper exploration of how process philosophy connects to AI creativity

[3] Geoffrey Engelstein, Isaac Shalev. Building Blocks of Tabletop Game Design: An Encyclopedia of Mechanisms. CRC Press, 2022.

• The definitive taxonomy of game mechanisms, and the source for GameGrammar’s mechanism library

[4] Natalya F. Noy, Deborah L. McGuinness. Ontology Development 101. Stanford University.

• How to build structured vocabularies for complex domains

[5] Benny Cheung. Unlocking the Secrets of Tabletop Games Ontology. bennycheung.github.io, Feb 2025.

• Where the structured game vocabulary began, analyzing 2,000+ published games

[6] BoardGameGeek. The largest board game database and community.

• Source for the 870+ game reference library used in generation and originality scoring

[7] Dynamind Research. AI consulting and product development studio.

• Creator of GameGrammar