Representing and acquiring geographic knowledge
Representing and acquiring geographic knowledge
Various views on spatial prepositions
AI Magazine
Representations for Rigid Solids: Theory, Methods, and Systems
ACM Computing Surveys (CSUR)
Maintaining knowledge about temporal intervals
Communications of the ACM
Epistemological problems of artificial intelligence
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
SMA '91 Proceedings of the first ACM symposium on Solid modeling foundations and CAD/CAM applications
On the consistency of cardinal direction constraints
Artificial Intelligence
Spatiotemporal reasoning for smart homes
Designing Smart Homes
Exploiting qualitative spatial neighborhoods in the situation calculus
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
A qualitative trajectory calculus and the composition of its relations
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
International Journal of Ambient Computing and Intelligence
Transition constraints: a study on the computational complexity of qualitative change
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most geometric models are quantitative, making it difficult to abstract the underlying spatial information needed for tasks such as planning, learning or vision. Furthermore, the precision used in a typical quantitative system often exceeds the actual accuracy of the data. In this work we describe a systematic representation that builds spatial maps based on local qualitative relations between objects. It derives relations that are more "functionally relevant" - i.e. those that involve accidental alignments, or can be described based on such alignments. In one dimension, interval logic (Allen 83] provides a mechanism for representing these type of relations; in this work we propose a formalism that enables us to perform alignment-based reasoning in two and higher dimensions with objects at angles. The principal advantages of this representation is that a) it is free of subjective bias, and b) it is complete in the qualitative sense of distinguishing all overlap/ tangency/nocontact geometries. In addition, the model is capable of handling uncertainty in the initial system (e.g. "the fuse box is somewhere behind the compressor") by constructing bounded inferences from disjunctive input data. Two kinds of uncertainty can be handled - those arising from deliberate imprecision in the interest of compactness ("down the road from"), or those caused by an inadequacy of data (sensors, spatial descriptions, or maps).