Generating Relation Algebras for Qualitative Spatial Reasoning
Advances in Artificial Intelligence
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First-order logic is symbolized reasoning in which each sentence, or statement, is broken down into a subject and a predicate. The predicate modifies or defines the properties of the subject. Apart from the formal properties, qualitative spatial representation and reasoning is an important criterion for choosing a set of spatial relations that humans perceive and choose the same relations for distinguishing spatial configurations. Since its earliest inception many theories have been proposed for mereotopology in AI among which Region Connection Calculus (RCC) is most prominent. RCC provides an...
First-order logic is symbolized reasoning in which each sentence, or statement, is broken down into a subject and a predicate. The predicate modifies or defines the properties of the subject. Apart from the formal properties, qualitative spatial representation and reasoning is an important criterion for choosing a set of spatial relations that humans perceive and choose the same relations for distinguishing spatial configurations. Since its earliest inception many theories have been proposed for mereotopology in AI among which Region Connection Calculus (RCC) is most prominent. RCC provides an axiomatization of space which takes regions as primitive, rather than as constructions from sets of points. The expressiveness of the RCC in relational logic is far greater than the original 8 RCC base relations might suggest. In my thesis I contemplated ways to automatically generate representable relational algebras using spatial data based on RCC. Contrary to physical theories about space and time, my research based on mereotopological calculi permits rather inexpensive reasoning about entities located in space and time. For e.g. usage in handling spatial GIS queries and robot navigation.
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