CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
The role of knowledge in next-generation product development systems
Journal of Computing and Information Science in Engineering
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Towards Design Learning Environments - I: Exploring How Devices Work
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Representing function: Relating functional representation and functional modeling research streams
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Analogical recognition of shape and structure in design drawings
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Discovering implicit constraints in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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In human designer usage, symbols have a rich semantics, grounded on experience, which permits flexible usage - e.g. design ideation is improved by meanings triggered by contrastive words. In computational usage however, symbols are syntactic tokens whose semantics is mostly left to the implementation, resulting in brittle failures in many knowledge-based systems. Here we ask if one may define symbols in computational design as {label,meaning} pairs, as opposed to merely the label. We consider three questions that must be answered to bootstrap a symbol learning process: (a) which concepts are most relevant in a given domain, (b) how to define the semantics of such symbols, and (c) how to learn labels for these so as to form a grounded symbol. We propose that relevant symbols may be discovered by learning patterns of functional viability. The stable patterns are information-conserving codes, also called chunks in cognitive science, which relate to the process of acquiring expertise in humans. Regions of a design space that contain functionally superior designs can be mapped to a lower-dimensional manifold; the inter-relations of the design variables discovered thus constitute the chunks. Using these as the initial semantics for symbols, we show how the system can acquire labels for them by communicating with human designers. We demonstrate the first steps in this process in our baby designer approach, by learning two early grounded symbols, tight and loose.