Need efficient solutions for spatial constraint consistency in real-time systems? This paper introduces a dimension graph approach to maintain Euclidean spatial constraints among 2D objects, addressing a critical need in applications like geographic information systems and battlefield visualization. It proposes a dimension graph representation to manage directional spatial constraints efficiently. The research projects spatial constraints onto X and Y dimensions, constructing a dimension graph on each dimension. This approach transforms consistency checking into a graph cycle detection problem, which can be solved in linear time complexity. This provides a more efficient solution than competing approaches, particularly when dealing with conjunctive constraints. Moreover, the proposed algorithm guarantees global consistency, making it suitable for applications where few disjunctions exist in the spatial constraints. The dimension graph representation proves to be efficient, and this approach is much more efficient than competing approaches when there are few disjunctions in the spatial constraints, which are often true in above applications.
This paper, published in the International Journal on Artificial Intelligence Tools, aligns perfectly with the journal's focus on innovative AI techniques and their practical applications. The proposed dimension graph approach for maintaining spatial constraints showcases an efficient algorithm relevant to the journal's emphasis on advanced AI tools.