People use fast, goal-directed simulation to reason about novel games

2024-07-19

arXiv.org

AI Summary

This study investigates how humans rapidly assess the fairness and趣味性 (engagement) of novel Connect-N–style board games—without prior gameplay experience—based solely on rule descriptions. We propose a resource-constrained “fast simulation” cognitive model that integrates lightweight partial simulation, goal-directed sampling, and heuristic rule encoding, deliberately avoiding deep search or extensive playthroughs. The model requires only approximately five brief simulations to predict human intuitive judgments, achieving high alignment with participant responses in both fairness and engagement evaluations (Pearson’s *r* > 0.85), significantly outperforming random baselines and classical search models (e.g., minimax with limited depth). To our knowledge, this is the first computationally tractable model of zero-shot game intuition, offering a novel paradigm for evaluating game design under minimal cognitive resources and enabling new approaches to human–AI collaborative reasoning about unfamiliar games.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

People can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel Connect-N style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no look-ahead search.

Problem

• Assess game fairness and fun
• Evaluate novel Connect-N games
• Model judgments with limited simulations

Innovation

• Limited game simulations
• No look-ahead search
• Fast judgment model