Adaptive Logics for Defeasible Reasoning Adaptive Logics for Defeasible Reasoning

Adaptive Logics for Defeasible Reasoning

Applications in Argumentation, Normative Reasoning and Default Reasoning

    • €87.99
    • €87.99

Publisher Description

This book presents adaptive logics as an intuitive and powerful framework for modeling defeasible reasoning. It examines various contexts in which defeasible reasoning is useful and offers a compact introduction into adaptive logics.

The author first familiarizes readers with defeasible reasoning, the adaptive logics framework, combinations of adaptive logics, and a range of useful meta-theoretic properties. He then offers a systematic study of adaptive logics based on various applications.

The book presents formal models for defeasible reasoning stemming from different contexts, such as default reasoning, argumentation, and normative reasoning. It highlights various meta-theoretic advantages of adaptive logics over other logics or logical frameworks that model defeasible reasoning. In this way the book substantiates the status of adaptive logics as a generic formal framework for defeasible reasoning.

GENRE
Non-Fiction
RELEASED
2013
29 November
LANGUAGE
EN
English
LENGTH
456
Pages
PUBLISHER
Springer International Publishing
PROVIDER INFO
Springer Science & Business Media LLC
SIZE
15.4
MB
Modality, Semantics and Interpretations Modality, Semantics and Interpretations
2015
Dynamic Logic. New Trends and Applications Dynamic Logic. New Trends and Applications
2023
Philosophical Logic: Current Trends in Asia Philosophical Logic: Current Trends in Asia
2017
Paraconsistent Logic: Consistency, Contradiction and Negation Paraconsistent Logic: Consistency, Contradiction and Negation
2016
Dynamics, Uncertainty and Reasoning Dynamics, Uncertainty and Reasoning
2019
Recent Trends in Philosophical Logic Recent Trends in Philosophical Logic
2014
Personalisierung mit Hilfe von Web-Mining Personalisierung mit Hilfe von Web-Mining
2004
Personalisierung mit Hilfe von Web-Mining Personalisierung mit Hilfe von Web-Mining
2012