Everyday life takes place in space and time, and spatial experience lies at the heart of our existence. Understanding how we conceive of spatial relationships, and how we solve spatio-temporal problems, is therefore key to understanding human cognition. Spatial cognition research has advanced considerably over the past decades, with major successes particularly in computational implementations of knowledge representation and reasoning methods. Still, a range of key issues continue to pose major challenges. The goal of this seminar is to discuss the various options for the formalisation, implementation, and automated solution of spatial problems including the following issues: the identification and specification of relevant concepts as expressed in human language; the development of a module for automated understanding of domain descriptions; the use of spatial structures and affordances for direct spatial problem solving; and, the development of an efficient planning system capable of providing feasible solutions to spatial problems. In this context, this Dagstuhl Seminar is going to address four major themes:
- Conceptualisation. How do humans conceptualise and mentally represent spatial problems? What is the role of high-level spatio-temporal structures for perceiving spatial problems, for manipulating spatial configurations, and for commonsense spatial problem solving?
- Formalisation. What would be a suitable formalism for commonsense problem solving that allows an accurate, flexible, and readable knowledge representation for spatio-temporal effects of actions performed by an intelligent agent?
- Description. In contrast to the formal representation investigated on Item 2, the present topic deals with the development of human readable descriptions of the inputs, reasoning steps and solutions of spatial problems. In particular, we want to investigate whether (and to what extent) it would be possible to develop high-level representations or interfaces for dealing with natural language and/or diagrammatic constructions that allow specifying both the input knowledge and the output conclusions in terms of descriptions of spatial problems.
- Problem solving. What are the commonsense problem-solving capabilities involving spatio-temporal features including temporal explanation and planning under physical/geometric qualitative or semi-quantitative constraints? This issue also includes the investigation of appropriate problem-solving algorithms and their potential applications to real-world domains that could be of interest to industry.
- artificial intelligence / robotics
- modelling / simulation
- semantics / formal methods
- Knowledge representation
- Problem Solving
- Spatial Reasoning
- Language analysis and cognitive processes