Temporal reasoning based on semi-intervals
Artificial Intelligence
Spatio-temporal data handling with constraints
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Maintaining knowledge about temporal intervals
Communications of the ACM
An Extended Algebra for Constraint Databases
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the International Workshop on Spatio-Temporal Database Management
STDBM '99 Proceedings of the International Workshop on Spatio-Temporal Database Management
Spatio-temporal Annotated Constraint Logic Programming
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
A Topological Calculus for Cartographic Entities
Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
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Motion can be seen as a form of spatio-temporal change. This concept is used in this paper to present a qualitative representational model for integrating qualitatively time and topological information for reasoning about dynamic worlds in which spatial relations between regions may change with time. The calculus here presented exploits Freksa's notion of conceptual neighbourhood and defines a topological neighbourhood of the topological calculus about point-like, linear and areal 2D regions defined by Isli, Museros et alters. According to this, two topological relations can be called conceptual neighbours if one can be transformed into the other one by a process of gradual, continuous change which does not involve passage through any third relation. The calculus described consists of a constraint-based approach and it is presented as an algebra akin to Allen's (1983) temporal interval algebra. One advantage of presenting the calculus in this way is that Allen's incremental constraint propagation algorithm can then be used to reason about knowledge expressed in the calculus. The algorithm is guided by composition tables and a converse table provided in this contribution. The algorithm will help, for instance, during the path-planning task of an autonomous robot by describing the sequence of topological situations that the agent should find during its way to the target objective.