A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Navigation and mapping in large-scale space
AI Magazine
Analogical representations of naive physics
Artificial Intelligence
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Qualitative spatial reasoning: the CLOCK project
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Image and brain: the resolution of the imagery debate
Image and brain: the resolution of the imagery debate
Extracting and representing qualitative behaviors of complex systems in phase space
Artificial Intelligence
Automated reasoning about machines
Artificial Intelligence
Realization of a geometry-theorem proving machine
Computers & thought
Towards a Theory of Commonsense Visual Reasoning
Proceedings of the Tenth Conference on Foundations of Software Technology and Theoretical Computer Science
Taming Chaotic Circuits
Reasoning about fluid motion I: finding structures
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
History-based interpretation of finite element simulations of seismic wave fields
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An architecture for exploring large design spaces
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Qualitative analysis of distributed physical systems with applications to control synthesis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Influence-based model decomposition for reasoning about spatially distributed physical systems
Artificial Intelligence
Modeling Moving Objects over Multiple Granularities
Annals of Mathematics and Artificial Intelligence
Physics-Based Feature Mining for Large Data Exploration
Computing in Science and Engineering
Sampling Strategies for Mining in Data-Scarce Domains
Computing in Science and Engineering
Perceptual Organization as a Foundation for Graphics Recognition
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Phase-Space Nonlinear Control Toolbox: The Maglev Experience
Hybrid Systems V
Computational Discovery of Communicable Knowledge: Symposium Report
DS '01 Proceedings of the 4th International Conference on Discovery Science
``Seeing'' Objects in Spatial Datasets
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Feature Characterization in Scientific Datasets
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Artificial Intelligence Review
Qualitative simulation and related approaches for the analysis of dynamic systems
The Knowledge Engineering Review
Automated detection of qualitative spatio-temporal features in electrocardiac activation maps
Artificial Intelligence in Medicine
Relation-based aggregation: finding objects in large spatial datasets
Intelligent Data Analysis
Structure Discovery from Massive Spatial Data Sets Using Intelligent Simulation Tools
Computational Discovery of Scientific Knowledge
Spatial aggregation for qualitative assessment of scientific computations
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Gaussian process models of spatial aggregation algorithms
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Cognitive modelling of event ordering reasoning in imagistic domains
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Ontology for Imagistic Domains: Combining Textual and Pictorial Primitives
ER '09 Proceedings of the ER 2009 Workshops (CoMoL, ETheCoM, FP-UML, MOST-ONISW, QoIS, RIGiM, SeCoGIS) on Advances in Conceptual Modeling - Challenging Perspectives
Spatial aggregation: language and applications
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Electrocardiographic imaging: towards automated interpretation of activation maps
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
International Journal of Intelligent Information and Database Systems
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Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers - kam, maps, and hipair - in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.