A Qualitative Model for Reasoning about Path and Support
A hybrid qualitative reasoning model with center-of-mass stability logic enables explainable path-planning and stability guidance for the Camelot Jr. block-puzzle game.
The paper presents a hybrid qualitative reasoning (QR) model for Camelot Jr., a block-puzzle game requiring multi-level bridge construction between two avatars on separate towers. It integrates mathematical center-of-mass stability logic into a symbolic qualitative solver to handle the game's precise physics. Because QR models reason over symbolic representations, the agent translates game states into interpretable feedback, supporting human-like tutoring, player guidance, and spatial skill training for children.
- Integrates mathematical center-of-mass stability logic into a qualitative solver for precise game physics.
- Symbolic reasoning enables interpretable game-state feedback for human-like tutoring and player guidance.
- Targets spatial skill training in children through explainable game-playing agents.
Full article177 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.20349