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Perspectives on logics for data-driven reasoning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Perspectives on logics for data-driven reasoning/ edited by Hykel Hosni, Jürgen Landes.
其他作者:	Hosni, Hykel.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xiii, 207 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1. A note on logic and the methodology of data-driven science (Hosni and Landes) -- Chapter 2. Pure Inductive Logic (Vencovska) -- Chapter 3. Where do we stand on maximal entropy? (Williamson) -- Chapter 4. Probability logic and statistical relational artificial intelligence (Weitkamper) -- Chapter 5. An Overview of the Generalization Problem (Facciuto) -- Chapter 6. The Logic of DNA Identification (Zabell) -- Chapter 7. Reasoning With and About Bias (Manganini and Primiero) -- Chapter 8. Knowledge Representation, Scientific Argumentation and Non-monotonic Logic (Landes et al) -- Chapter 9. Reasoning with Data in the framework of a Quantum Approach to Machine Learning (Chiara et al).
Contained By:	Springer Nature eBook
標題:	Computer logic. -
電子資源:	https://doi.org/10.1007/978-3-031-77892-6
ISBN:	9783031778926

Perspectives on logics for data-driven reasoning
Perspectives on logics for data-driven reasoning[electronic resource] /edited by Hykel Hosni, Jürgen Landes. - Cham :Springer Nature Switzerland :2024. - xiii, 207 p. :ill. (some col.), digital ;24 cm. - Logic, argumentation & reasoning. Interdisciplinary perspectives from the humanities and social sciences,v. 352214-9139 ;. - Logic, argumentation & reasonin, interdisciplinary perspectives from the humanities and social sciences ;v. 35..

Chapter 1. A note on logic and the methodology of data-driven science (Hosni and Landes) -- Chapter 2. Pure Inductive Logic (Vencovska) -- Chapter 3. Where do we stand on maximal entropy? (Williamson) -- Chapter 4. Probability logic and statistical relational artificial intelligence (Weitkamper) -- Chapter 5. An Overview of the Generalization Problem (Facciuto) -- Chapter 6. The Logic of DNA Identification (Zabell) -- Chapter 7. Reasoning With and About Bias (Manganini and Primiero) -- Chapter 8. Knowledge Representation, Scientific Argumentation and Non-monotonic Logic (Landes et al) -- Chapter 9. Reasoning with Data in the framework of a Quantum Approach to Machine Learning (Chiara et al).

This book calls for a rethinking of logic as the core methodological tool for scientific reasoning in the context of a steadily increasing emphasis on data-centered science. To do so it provides a state-of-the-art presentation of the role logic can have in making the most of the current opportunities while making explicit the key challenges opened up by the data-driven age of scientific research. Particular attention is given to the following four core fields and applications: Reasoning with correlations (medical, life-science applications); logics for statistical inference (machine learning, and societal applications thereof); reasoning with evidence (defining good evidence); causal reasoning (forensic reasoning). The book collects contributions from key logicians, methodologists and scientists. This multidisciplinary perspective benefits both scientists and logicians interested in data-driven science. Scientists are introduced to logics that go beyond classical and thus are applicable to reasoning with data; Logicians have a change to focus on the potential applications of their methods and techniques to pressing scientific problems. This book is, therefore, of interest to scientists and logicians working on data-centered science.

ISBN: 9783031778926

Standard No.: 10.1007/978-3-031-77892-6doiSubjects--Topical Terms:

840463
Computer logic.

LC Class. No.: BC91

Dewey Class. No.: 161

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520 $a This book calls for a rethinking of logic as the core methodological tool for scientific reasoning in the context of a steadily increasing emphasis on data-centered science. To do so it provides a state-of-the-art presentation of the role logic can have in making the most of the current opportunities while making explicit the key challenges opened up by the data-driven age of scientific research. Particular attention is given to the following four core fields and applications: Reasoning with correlations (medical, life-science applications); logics for statistical inference (machine learning, and societal applications thereof); reasoning with evidence (defining good evidence); causal reasoning (forensic reasoning). The book collects contributions from key logicians, methodologists and scientists. This multidisciplinary perspective benefits both scientists and logicians interested in data-driven science. Scientists are introduced to logics that go beyond classical and thus are applicable to reasoning with data; Logicians have a change to focus on the potential applications of their methods and techniques to pressing scientific problems. This book is, therefore, of interest to scientists and logicians working on data-centered science.
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