Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Integrating machine learning with knowledge-based systems

Computational learning systems attempt to improve performance with experience, according to some pre-de ned performance dimensions. Many such systems induce rule sets that function as simple knowledge bases (e.g., Quinlan, 1986). Simon (1983) argued that learning can be more e cient than programming for large knowledge-based systems. Thus, hybrid systems involving large knowledge bases may pro ...

Integrating Models of Knowledge and Machine Learning

We propose a theoretical framework allowing a real integration of Machine Learning and Knowledge acquisition. This article shows that the input of a Machine Learning system can be mapped to the model of expertise as it is used in KADS methodology. The notion of learning bias will play a central role. We shall see that parts of it can be identified to what people are used to call the inference a...

Techniques for integrating machine learning with knowledge acquisition

Knowledge acquisition is frequently cited as the greatest bottleneck in the development of expert systems. Two primary approaches to knowledge acquisition are elicitation of knowledge from experts (traditional knowledge acquisition) and machine learning. Both approaches have strengths and weaknesses. Experts can draw upon practical experience and both domain specific and general knowledge. In a...

Practical Machine Learning for Software Engineering and Knowledge Engineering

Machine learning is practical for software engineering problems, even in datastarved domains. When data is scarce, knowledge can be farmed from seeds; i.e. minimal and partial descriptions of a domain. These seeds can be grown into large datasets via Monte Carlo simulations. The datasets can then be harvested using machine learning techniques. Examples of this knowledge farming approach, and th...

Integrating Knowledge, Performance, and Learning Systems