The structure-mapping engine: algorithm and examples
Artificial Intelligence
Image and brain: the resolution of the imagery debate
Image and brain: the resolution of the imagery debate
A functional theory of design patterns
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Visual style: Qualitative and context-dependent categorization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Transfer of problem-solving strategy using Covlan
Journal of Visual Languages and Computing
Proteus: Visuospatial analogy in problem-solving
Knowledge-Based Systems
Modeling Cross-Cultural Performance on the Visual Oddity Task
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Analogical learning of visual/conceptual relationships in sketches
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A cognitive model of visual analogical problem-solving transfer
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Protein structure prediction with visuospatial analogy
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
Combining concept maps to catalyze creativity
C&C '11 Proceedings of the 8th ACM conference on Creativity and cognition
A computational model of visual analogies in design
Cognitive Systems Research
Exploiting persistent mappings in cross-domain analogical learning of physical domains
Artificial Intelligence
Fractal analogies for general intelligence
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
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Computational models of analogical problem solving have traditionally described source and target domains in terms of their causal structure. But psychological research shows that visual reasoning plays a part for many kinds of analogies. This paper describes a model that transfers a solution from a source analog to a new target problem using only visual knowledge represented symbolically. The knowledge representation is based on a language of primitive visual elements and transformations. We found that visual knowledge is sufficient for transfer, but that causal knowledge is needed to determine if the transferred solution is appropriate.