データ駆動推論のための論理学の視座 Perspectives on Logics for Data-driven Reasoning (Logic, Argumentation & Reasoning)

Hosni, Hykel (EDT)/ Landes, Jürgen (EDT)

Springer International Publishing AG（2025/02発売）

• 製本 Hardcover:ハードカバー版／ページ数 207 p.
• 言語 ENG
• 商品コード 9783031778919

Full Description

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.

Contents

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).