A blackboard architecture for control
Artificial Intelligence
Design by derivational analogy: issues in the automated replay of design plans
Artificial Intelligence
Formalism in AI and computer science
Formalism in AI and computer science
LEAP: a learning apprentice for VLSI design
Machine learning
Automated reuse of design plans in BOGART
Artificial intelligence in engineering design (Vol. II)
Argo: an analogical reasoning system for solving design problems
Artificial intelligence in engineering design (Vol. II)
Using exploratory design to cope with design problem complexity
Artificial intelligence in engineering design (Vol. II)
Design as top-down refinement plus constraint propagation
Artificial intelligence in engineering design (Volume I)
Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning
Machine Learning - Special issue on multistrategy learning
Machine learning of design concepts
Machine learning of design concepts
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
The "What" and "How" of Learning in Design
IEEE Expert: Intelligent Systems and Their Applications
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Reconstructive derivational analogy: an approach to redesign
Reconstructive derivational analogy: an approach to redesign
A data mining-based engineering design support system: a research agenda
Data mining for design and manufacturing
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Knowledge transformers: A link between learning and creativity
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Information generation during design: Information importance and design effort
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A function–behavior–structure ontology of processes
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, “What types of knowledge can be learnt?”, “How does learning occur?”, and “When does learning occur?”. Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD.