Perspectives on Logics for Data-driven Reasoning
Author:   Hykel Hosni ,  Jürgen Landes
Publisher:   Springer International Publishing AG
ISBN:  

9783031778940


Pages:   207
Publication Date:   22 January 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
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Perspectives on Logics for Data-driven Reasoning


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Author:   Hykel Hosni ,  Jürgen Landes
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
ISBN:  

9783031778940


ISBN 10:   3031778944
Pages:   207
Publication Date:   22 January 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of 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).

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Author Information

Hykel Hosni is professor of Logic at the Department of Philosophy at University of Milan, and currently head of the Logic, Uncertainty, Computation, and Information (LUCI) Lab. He contributed to the logical foundations of reasoning and decision-making under uncertainty. His main current interest lies with the logical foundation of data-intensive and AI-driven science. Jürgen Landes is a researcher Munich Center for Mathematical Philosophy at the LMU Munich. His work spans a wide variety of problems, approaches and techniques related to uncertain inference. In particular, he contributed to Pure Inductive Logic, the Principle of Maximum Entropy, general Bayesian inference and Bayesian inference in medicine.

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