In order to solve problems effectively, a problem solver must be able to exploit domain-specific search control knowledge. Although previous research has demonstrated that explanation-based learning is a viable approach for acquiring such knowledge, in practice the control knowledge learned via EBL may not be useful. To be useful, the cumulative benefits of applying the knowledge must outweigh the cumulative costs of testing whether the knowledge is applicable. Unlike most previous systems that use EBL, the PRODIGY system evaluates the costs and benefits of the control knowledge it learns. Furthermore, the system produces useful control knowledge by actively searching for “good” explanations—explanations that can be profitably employed to control problem solving.
A question a lot of ML practitioners get asked a frequently is: "What can I do to start being able to actually build Machine Learning projects and solutions?" There is so much information out there -- both good and bad -- that it can be hard to know where to begin. Also, people come from very different backgrounds, so the starting point can vary significantly. For example, for me, I entered the ML world by watching theoretical videos from Computer Science channels about neural networks, and as I got more and more interested I started reading articles, news, and blogs about the topic. However, by doing this I only developed a vague understanding of the most superficial part of Machine Learning, and I was nowhere near being able to tackle a project by myself.
This paper focuses on care support knowledge (especially focuses on the sleep related knowledge) and tackles its cognitive bias and humanity aspects from machine learning perspective through discussion of whether machine learning can correct commonly accepted knowledge and provide understandable knowledge in care support domain. For this purpose, this paper starts by introducing our data mining method (based on association rule learning) that can provide only necessary number of understandable knowledge without probabilities even if its accuracy slightly becomes worse, and shows its effectiveness in care plans support systems for aged persons as one of healthcare systems. The experimental result indicates that (1) our method can extract a few simple knowledge as understandable knowledge that clarifies what kinds of activities (e.g., rehabilitation, bathing) in care house contribute to having a deep sleep, but (2) the apriori algorithm as one of major association rule learning methods is hard to provide such knowledge because it needs calculate all combinations of activities executed by aged persons.
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3,4,5,6,7,8,9,10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11,12,13 (vector representations of words) without human labelling or supervision.
Knowledge engineering is the process of creating rules that apply to data in order to imitate the way a human thinks and approaches problems. A task and its solution are broken down to their structure, and based on that information, AI determines how the solution was reached. Often, a library of problem-solving methods and knowledge to solve a particular set of problems is fed into a system as raw data. Then, the system can diagnose the problem and find the solution without further human input. The result can be used as a self-help troubleshooting software, or as a support module to a human agent.