People use fast, goal-directed simulation to reason about novel games (2407.14095v2)

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.

• The paper introduces a model demonstrating that human-like, resource-limited simulations accurately predict game outcomes and perceived fun.
• It leverages heuristic strategies based on position utility, goal progression, and opponent blocking to mimic novice decision-making.
• The findings highlight that quick, partial simulations provide robust insights for evaluating fairness and guiding engaging game design.

Analyzing Mechanisms of Fast, Goal-Directed Simulation in Reasoning About Novel Games

The paper "People use fast, goal-directed simulation to reason about novel games" addresses the cognitive mechanisms underlying individuals' ability to quickly and effectively reason about unfamiliar decision-making scenarios, particularly in the context of novel connect-n style board games. The authors present an innovative model that mimics human judgment and decision-making through limited, goal-directed game simulations.

Overview

The research diverges from traditional studies of gameplay focusing on optimality and expertise developed through extensive experience. Instead, this paper probes how individuals form judgments about the fairness and enjoyability of games from minimal prior exposure. The underlying assertion is that people utilize fast and resource-limited simulations to make these evaluations.

The paper primarily centers on a diverse dataset of 121 novel grid-based connect-n games, examining human predictions of game outcomes and the perceived fun calculated from brief contemplation and limited interactions. The model developed in this research simulates these judgments using a small number of partial game simulations and minimal lookahead search, aligning well with observed human data.

Model Components

The core of the model is a simulated agent, which operates under specific general utility functions based on heuristic strategies to place utility on intermediate game states:

1. Position Utility: Evaluates the centrality of a move, hypothesizing that placing pieces closer to the center generally allows them to participate in more potential winning configurations.
2. Goal Progression: Considers the maximum contiguous pieces a move would create, reflecting the drive towards reaching a win condition.
3. Opponent Blocking: Assesses the extent to which a move hinders the opponent's progress towards their winning goals.

These utility functions collectively emulate the goal-directed nature of a novice player and derive probabilistic inferences based on partial simulations rather than exhaustive searches.

Methodology

Human participants, recruited via Prolific, were tasked with evaluating a selection of novel games. Participants estimated the likelihood of various game outcomes and rated the perceived fun of the games based on brief exposure and minimal interactive simulations using a scratchpad tool. The human-derived data was then compared against the model's predictions.

The paper also tested several alternative models encompassing simpler random agent simulations, more computationally intensive search-based simulations, and estimates drawn from LLMs. The comparisons demonstrated that the proposed model of limited but goal-directed simulation aligns more closely with human judgments than both more simplistic and more optimal agent models.

Results and Implications

The model accurately predicts human judgments about game outcomes and perceptions of fun, supporting the notion that humans utilize fast, probabilistic inferences derived from limited simulations to evaluate novel scenarios. The findings suggest that while optimal, deep search methods might perform better in terms of achieving game success, they do not necessarily reflect the intuitive and rapid assessments that humans make in unfamiliar contexts.

Furthermore, the model's predictive capability is evident in various game subcategories, showcasing its robustness across different game dynamics, board configurations, and win conditions. The incorporation of fairness and expected game length as factors influencing perceived fun highlights crucial elements in game design and human preference.

Future Directions

Several avenues for further research are proposed. These include examining how people internalize the constraints and goals of novel tasks from linguistic descriptions, integrating LLMs for improved semantic parsing, and extending the model to paper the rapid skill acquisition as individuals progress from novices to experts in novel domains. Additionally, this research contributes to broader discussions on resource rationality and efficient planning in cognitive science, emphasizing the balance between computational efficiency and effective decision-making.

Overall, this paper provides significant insights into the cognitive processes behind quick evaluations of novel problems, with applications extending beyond gameplay to general problem-solving and decision-making in uncertain environments. The model's alignment with human behavior underscores its potential as a foundational tool in both theoretical investigations and practical applications in AI development, cognitive modeling, and human-computer interaction.