发表机构
Umeå University（于默奥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对Camelot Jr.积木拼图游戏，提出一种结合质心稳定性逻辑的混合定性推理模型，以支持空间技能训练和可解释的游戏智能体开发。
AI 中文摘要
空间推理能力与STEM领域的表现密切相关。游戏为培养儿童这些关键技能提供了一种引人入胜的媒介，而儿童天生就喜欢玩耍。然而，为了实现类似人类的辅导和玩家引导，这些游戏需要一个能够从空间事件中进行常识推理的AI智能体。定性推理（QR）模型似乎是这些应用领域的合适框架。由于这些模型以符号表示进行推理，它们可以无缝地将游戏状态转换为可解释的反馈，用于类似人类的玩家引导。本文介绍了一种为Camelot Jr.设计的混合定性模型，Camelot Jr.是一款积木拼图游戏，要求构建多层桥梁以连接两个位于不同塔楼上的化身。该游戏对玩家提出了挑战，玩家必须使平台稳定、规划路径，并确保使用所有提供的积木。为了处理该领域所需的精确物理，我们集成了一个数学质心稳定性逻辑来指导我们的定性求解器。我们的工作促进了Camelot Jr.中的空间技能训练，并有助于开发以人为中心、可解释的游戏智能体。
英文摘要
Spatial reasoning abilities correlate strongly with performance in STEM fields. Games offer a compelling medium for training these critical skills in developing children who have a natural proclivity for play. However, to facilitate human-like tutoring and player guidance, these games require an AI agent capable of making commonsense inferences from spatial events. Qualitative reasoning (QR) models appear to be a suitable framework for these application domains. As these models reason in symbolic representations, they can seamlessly translate game states into interpretable feedback for human-like player guidance. This paper introduces a hybrid qualitative model designed for Camelot Jr., a block-puzzle game that requires constructing multi-level bridges to connect two avatars stationed on separate towers. The game poses a challenge for the player, who must make platforms stable, plan their path, and ensure they use all the provided blocks. To handle the precise physics required by the domain, we integrate a mathematical center-of-mass stability logic to guide our qualitative solver. Our work facilitates spatial skill training in Camelot Jr. and contributes to the development of human-centric, explainable game-playing agents.
CommentsWorkshop on Qualitative Reasoning 2026 at IJCAI (35th International Joint Conference on Artificial Intelligence)
