Abstract Concept Modelling in Conceptual Spaces: A Study on Chess Strategies
This work provides a foundation for applications in sequential decision-making, though it is incremental in extending conceptual spaces theory to temporal concepts.
The authors tackled the problem of modeling abstract concepts that unfold over time by developing a conceptual space framework, demonstrated through chess strategies like attack or sacrifice, with results showing that movement patterns in the framework align with expert commentary.
We present a conceptual space framework for modelling abstract concepts that unfold over time, demonstrated through a chess-based proof-of-concept. Strategy concepts, such as attack or sacrifice, are represented as geometric regions across interpretable quality dimensions, with chess games instantiated and analysed as trajectories whose directional movement toward regions enables recognition of intended strategies. This approach also supports dual-perspective modelling, capturing how players interpret identical situations differently. Our implementation demonstrates the feasibility of trajectory-based concept recognition, with movement patterns aligning with expert commentary. This work explores extending the conceptual spaces theory to temporally realised, goal-directed concepts. The approach establishes a foundation for broader applications involving sequential decision-making and supports integration with knowledge evolution mechanisms for learning and refining abstract concepts over time.