With respect to its basic Research Topics, the contemporary scientific agenda of STeDy addresses several areas within Knowledge Representation and Reasoning (KR) in particular, and reaches out to all sub-disciplines within Artificial Intelligence, Spatial Cognition, and Cognitive Technologies in general. Some core KR areas being addressed include:
Spatial Representation and Reasoning
Commonsense Reasoning
Qualitative Reasoning
Reasoning about Actions and Change
Logic-based Modelling of Dynamic Systems
Language, Ontology, and Space
Spatial Learning
Cognitive Technologies and Spatial Cognition Other areas of AI where Space, Time, Events, Actions, Change, Causality, Dynamics, Spatio-Temporal Learning play an important role are welcome to contribute to all initiatives within STeDy. If in doubt, please do not hesitate to directly email the responsible contact person(s).
Main topics of interest include:
Representing and reasoning about dynamic spatial systems
Non-monotonic reasoning in dynamic spatial systems
Abduction for spatial knowledge discovery
Spatio-Temporal Abduction, Causal explanation with spatio-temporal data
Declarative spatial representation and reasoning, with:
- Logic Programming, Abductive Logic Programming
- Constraint Logic Programming
- Answer-Set Programming
Qualitative reasoning
Qualitative spatial representation and reasoning
Qualitative spatio-temporal continuity
Visual and diagrammatic reasoning
Spatio-Temporal belief revision
Non-Monotonic causal formalizations of spatio-temporal change
Spatial and physical reasoning
Concurrency in the spatial domain
Integrated approaches for modelling and reasoning about space, actions and change
Event-based Modelling
Spatio-Temporal Narratives
Spatio-Temporal Query Languages
Commonsense ontologies of spatio-temporal dynamics
Integrated spatial and ontological reasoning
Ontologies for spatial scene descriptions (e.g., indoor environments, room-space)
Grounding processes for perceptual environmental data with spatial ontologies
Space, Motion, Natural Language
Mapping of NL to spatial representation and reasoning
Geometric Space, Mereology, Mereogeometry
Shape and 3D space
Commonsense and Qualitative models of visibility
Qualitative abstractions of motion (e.g., for robotics, events and processes in geospatial dynamics)
Learning of commonsensical, qualitative spatial representations
Hybrid qualitative-qualitative reasoning
Qualitative abstractions for Machine Learning
Planning, explanation, simulation, and learning within dynamic spatial systems Some applications areas of special interest that have also witnessed recent attention include:
Spatial Computing for Design (e.g., diagnosis, requirement consistency)
Cognitive Robotics (e.g., high-level spatial planning & control)
Geospatial Dynamics (e.g., reasoning with events and processes in GIS)
Behaviour and Activity Interpretation
Ambient Intelligence and Smart Environments
Computer-Aided Learning (CAL) (e.g., question-answering, NLU applications)
Computer-Aided Design (CAD)
Sketch Recognition and Understanding
Location-based Services
Real-time Systems (e.g., traffic, transportation)
Assistive technologies dealing with spatio-temporal Information
Commonsense notions of space & change in biology, physics, and chemistry
Hybrid Intelligent Sytems with a Spatial, Temporal or Spatio-Temporal component