A Qualitative Model for Reasoning about Path and Support

2026-09-17

AI Summary

本文提出了一种混合定性模型，用于解决Camelot Jr.游戏中路径规划与支撑稳定性问题，通过结合质心稳定逻辑来指导游戏中的空间技能训练。

Abstract

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.

Problem

• Spatial reasoning
• Games
• AI agent
• Qualitative reasoning
• Camelot Jr.

Innovation

• qualitative reasoning
• spatial reasoning
• center-of-mass stability
• human-like tutoring
• explainable game-playing agents