The need to support computational reasoning about space, however, in application areas such as motion planning and manipulation in physical space, the indexing and retrieval of images, geographic information systems, diagrammatic reasoning, and the design of high-level graphics programs has led to new interest in spatial representations and spatial reasoning.
If reasoning about space depended on first indexing specific objects, it would hold little utility in novel situations.
This chapter presents a set of challenges as a pointer to a research agenda that includes, among other things, generalization of global datasets, better evaluation methodologies, and better integration of reasoning about space in order to support autonomous solutions in map generalization.
Combined logical reasoning about space-time has been actively evolving over the past decades, particularly in the context of Artificial Intelligence, spatio-temporal databases, ontologies and constraint networks.
This paper argues that previous work on alternative representations of version spaces has disguised the real question underlying version space reasoning.
Indeed, we show that tractable version space reasoning is possible if and only if there is an efficient algorithm for the consistency problem.
Our observations give rise to new concept classes for which tractable version space reasoning is now possible, e.g., 1-decision lists, monotone depth two formulas, and halfspaces.
Bayesian analogical reasoning : Consider a space of latent functions in 𝒜×ℬ→{0, 1}.
We instead show that tractable reasoning with version spaces turns out to depend on the consistency problem, i.e., determining if there is any concept consistent with a set of positive and negative examples.
The centerpiece of NASA's reasoning is that the space station is needed as a laboratory for testing the effects of long-duration space flight on humans.
