Design synthesis knowledge and inductive machine learning

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM

Volumn 15, Issue 3, 2001, Pages 233-249

  Potter, S ^a   Darlington, M J ^a   Culley, S J ^a   Chawdhry, P K ^a  

^a University of Bath   (United Kingdom)

Author keywords

Conceptual design; Design synthesis; Heuristic knowledge; Inductive machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; COMPUTER AIDED DESIGN; COMPUTER SYSTEMS; HEURISTIC METHODS; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS;

CONCEPTUAL DESIGN; DESIGN SYNTHESIS; ENGINEERING DESIGN; HEURISTIC KNOWLEDGE; INDUCTIVE MACHINE LEARNING; INTELLIGENT COMPUTER SYSTEMS;

LEARNING SYSTEMS;

EID: 0035358446     PISSN: 08900604     EISSN: None     Source Type: Journal    
DOI: 10.1017/S0890060401153047     Document Type: Article

References (31)

1. The problem of implicit knowledge
2. Rule induction with CN2: Some recent improvements
3. The CN2 induction algorithm
4. A connectionist view of creative design reasoning
5. The 'what' and 'how' of learning in design
6. Special issue: Machine learning in design
7. Special issue: Machine learning in design
8. Sharing the learning activity using intelligent CAD
9. Knowledge acquisition via incremental conceptual clustering
10. Knowledge acquisition for expert systems
11. A neural network-based machine learning approach for supporting synthesis
12. The role of heuristics in learning by discovery: Three case studies
13. A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision making
14. Special issue: Machine learning in design
15. Case-based reasoning in design
16. Learning design concepts using machine learning techniques
17. R1: A rule-based configurer of computer systems
18. The state of the art in machine learning
19. Towards a generic model of configuration tasks
20. The potential of machine learning techniques for expert systems
21. Inductive learning of synthesis knowledge
22. A system that learns to design cable-stayed bridges
23. Learning internal representations by error propagation