Endowing computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using representations based on formal logic or other formal representations. The challenges to creating such a formalization include the accumulation of large amounts of knowledge about our everyday world, the representation of this knowledge in suitable formal languages, the integration of different representations in a coherent way, and the development of reasoning methods that use these representations.
Reasoning about space and time is a major field of interest in many areas of theoretical and applied AI, especially in the theory and application of temporal and spatial models in planning, high-level navigation of autonomous mobile robots, natural language understanding, temporal databases, and concurrent and distributed programming. The Spatio-Temporal Reasoning special track at FLAIRS focuses on research and development aspects in the area of reasoning about models of space and time.
A sizable fraction of current research into human visuo-spatial knowledge processing explicitly or implicitly suggests a spatial processing of certain knowledge types and a visual processing of others. Similarly, many formal and technical approaches for representing and processing visuo-spatial information in artificial intelligence, in computational cognitive modeling, or in knowledge representation and reasoning explicitly or implicitly treat visual and spatial information as belonging to separate types. While there exists good evidence for some differences in mental processing of different visuo-spatial knowledge types, there is much less reason to maintain the currently ascribed separation between the visual and the spatial. We provide arguments on why strict dichotomies seem unwarranted with regard to descriptions of human mental spatial reasoning and disadvantageous for the formal and technical approaches. We build upon a synopsis of psychological evidence for the existence of multiple knowledge type specific representations in human visuo-spatial reasoning and discuss the notion of scalable representation structures. In absence of proof to the contrary, it seems better practice to assume that (a) many of the type differences attributed to visuo-spatial knowledge processing are gradual rather than qualitative in nature, and that (b) tasks involving visuo-spatial knowledge of several types are often mentally processed through dynamic associations of structures for processing basal knowledge types. The paper calls for more investigations of human reasoning in visuo-spatial tasks in which knowledge types dynamically change during reasoning. It outlines a research framework for systematically investigating different basal visuo-spatial knowledge types and their combinations with regard to cognitive and computational plausibility. Current research is related to the framework, including research on Casimir, our computational cognitive architecture for reasoning with visuo-spatial knowledge. We argue that a more systematic course of research along the lines of the proposed framework will not only lead to more appropriate descriptions of human cognition (regarding visuo-spatial knowledge processing) but may also spawn more integrated and versatile formal and technical approaches for dealing with visuo-spatial information.