Abstract Concept Modelling in Conceptual Spaces: A Study on Chess Strategies

2026-01-29

AI Summary

This study addresses the modeling and recognition of abstract strategies—such as aggressive play or sacrificial moves in chess—that evolve over time within a conceptual space. Grounded in conceptual space theory, the work represents strategies as dynamic geometric regions along interpretable quality dimensions and infers strategic intent from the directional movement of game trajectories. The paper innovatively extends conceptual spaces to accommodate temporally evolving, goal-directed abstract concepts, introducing a trajectory-driven strategy recognition mechanism and a dual-perspective cognitive interpretation framework to capture divergent player understandings of identical board states. Experimental results demonstrate that the identified strategic motion patterns align closely with expert commentary, thereby validating the approach’s effectiveness and interpretability in sequential decision-making contexts.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• abstract concepts
• conceptual spaces
• temporal modelling
• strategy recognition
• sequential decision-making

Innovation

• conceptual spaces
• abstract concept modeling
• trajectory-based recognition
• temporal concepts
• dual-perspective modeling