Knowledge representation and inductive reasoning using conditional logic and sets of ranking functions

Knowledge representation and inductive reasoning using conditional logic and sets of ranking functions /

Saved in:
Bibliographic Details
Main Author: Kutsch, Steven (Author)
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: Amsterdam, The Netherlands : Berlin : IOS Press B.V. ; Akademische Verlagsgesellschaft, [2021]
Series:Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence.
Frontiers in artificial intelligence and applications ; v. 350.
Subjects:
Artificial intelligence.
Reasoning > Mathematical models.
Induction (Logic) > Mathematical models.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: ch. 1 Introduction
  • 1.1. Context and Motivation
  • 1.2. Research Questions and Contributions
  • 1.3. Outline
  • 1.4. Previous Publications
  • ch. 2 Background
  • 2.1. Logical Preliminaries
  • 2.2. From Preferential Inference to Plausibility Measures
  • 2.3. Ranking Functions
  • 2.4. Inductive Reasoning in System Z
  • ch. 3 Inference Using Sets Of Ranking Functions
  • 3.1. Modes of Inference
  • 3.2. C-Representations and C-Inference
  • 3.3. Interrelationships of Inference Systems
  • ch. 4 Classification Of Conditionals For Calculating Closures Of Inference Relations
  • 4.1. Classes of Conditionals
  • 4.2. Complete Inference Relations
  • ch. 5 Inference Cores And Redundant Conditionals
  • 5.1. Inference Cores for Comparing Inference Relations
  • 5.2. Structural Inference and Redundant Conditionals
  • ch. 6 Maximal Impacts For C-Inference
  • 6.1. Regular and Sufficient Maximal Impacts
  • 6.2. Lower and Upper Bounds for Regular and Sufficient Maximal Impacts
  • ch. 7 Compact Representations Of Knowledge Bases For Optimising C-Inference
  • 7.1. Representing C-Inference as CSPs
  • 7.2. Compact Representation of Static Knowledge Bases
  • 7.3. Computational Benefits
  • 7.4. Compact Representation of Evolving Knowledge Bases
  • ch. 8 Formal Properties And Evaluation Of Non-Monotonic Inference Relations
  • 8.1. Skeptical Inference
  • 8.2. Credulous Inference
  • 8.3. Weakly Skeptical Inference
  • 8.4. Rationality of C-Inference Relations
  • 8.5. Empirical Evaluation of Nonmonotonic Inference Relations
  • ch. 9 Infocf: Implementing Inference Over Sets Of Ranking Models
  • 9.1. InfOCF-Lib
  • 9.2. Implementing EvaluateKBs(RM)
  • 9.3. Applications, Expansions and Future Work
  • ch. 10 Conclusions, Open Questions And Final Remarks
  • 10.1. Summary
  • 10.2. Future Work and Outlook.

Similar Items