Generating Relation Algebras for Qualitative Spatial Reasoning: Advances in Artificial Intelligence

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.

Prathap Siddavaatam received his B.Eng degree in 2001. He has worked in different domains of software development in India, Germany and USA. He moved to Canada in 2009 and entered Master of Science program at Brock University, St.Catharines graduating in Aug 2011. He is currently pursuing his Ph.D in Signal Processing at Ryerson University, Toronto.