Some views on the state of the art in artificial intelligence
Artificial intelligence and expert systems
The essence of knowledge engineering
Artificial intelligence and expert systems
Selecting knowledge acquisition tools and strategies based on application characteristics
International Journal of Man-Machine Studies
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Network Engineering for Agile Belief Network Models
IEEE Transactions on Knowledge and Data Engineering
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian networks in planning a large aquifer in Eastern Mancha, Spain
Environmental Modelling & Software
Environmental Modelling & Software
Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment
Environmental Modelling & Software
Toward a Theory of Knowledge Reuse: Types of Knowledge Reuse Situations and Factors in Reuse Success
Journal of Management Information Systems
Shape-based searching for product lifecycle applications
Computer-Aided Design
International Journal of Intelligent Information and Database Systems
Knowledge based engineering and intelligent personal assistant context in distributed design
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
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This paper presents a new causal design knowledge evaluation method and system for future CAD applications. Current product development processes still include unintended feedback due to insufficient product design knowledge. Previous research on design knowledge support system focuses on search by matching keywords and file names, or search by specific indices, which has various drawbacks. Furthermore, current CAD systems need manual input to incorporate the designer's knowledge. To systematize the knowledge management process for the next-generation CAD systems, a prerequisite is to capture ever-evolving causal design knowledge. In this paper, we present a new causal knowledge network evaluation method, which has not been well addressed in design knowledge support system research. For the network evaluation, we present a degree of causal representation (DCR)-based knowledge network evaluation method. In this method, causality and network connectivity are used for the causal knowledge network with weighted vertices and weighted network connectivity for a network with weighted edges. To validate the proposed method, this evaluation method has been compared with structural measures. Finally, the causal design knowledge evaluation system, KNOES, is implemented and tested with a new valve design scenario.