Hybrid model aids spatial reasoning in block puzzle game
A Qualitative Model for Reasoning about Path and Support
Artificial Intelligence
Summary
Spatial reasoning skills are important for success in science and technology. The authors created a special computer model to help a block-building game called Camelot Jr. teach these skills by understanding the stability of bridges players build. Their model uses simple ideas about space combined with math about balance to guide players better and explain what’s happening in the game. This helps players learn while playing and supports making smarter game helpers that can talk like human tutors.
What this means in practice
- •For game developers: Integrate the hybrid qualitative model to produce understandable player feedback and guidance in spatial puzzle games.
- •For educational software designers: Design training games that build spatial skills by embedding AI agents capable of commonsense spatial inferences about stability and paths.
Authors
Abhishek Jaiswal, Zoe Falomir
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.