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

A core problem in Artificial Intelligence is the modeling of human reasoning. Classic-logical approaches are too rigid for this task, as deductive inference yielding logically correct results is not appropriate in situations where conclusions must be drawn based on the incomplete or uncertain knowle...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kutsch, Steven
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : IOS Press, 2021.
Colección:Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence ; v. 350.
Temas:
Artificial intelligence.
Knowledge representation (Information theory)
Reasoning.
Intelligence artificielle.
Représentation des connaissances.
artificial intelligence.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title Page
  • Contents
  • Chapter 1. Introduction
  • 1.1 Context and Motivation
  • 1.2 Research Questions and Contributions
  • 1.3 Outline
  • 1.4 Previous Publications
  • Chapter 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
  • Chapter 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
  • Chapter 4. Classification of Conditionals for Calculating Closures of Inference Relations
  • 4.1 Classes of Conditionals
  • 4.2 Complete Inference Relations
  • Chapter 5. Inference Cores and Redundant Conditionals
  • 5.1 Inference Cores for Comparing Inference Relations
  • 5.2 Structural Inference and Redundant Conditionals
  • Chapter 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
  • Chapter 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
  • Chapter 8. Formal Properties and Evaluation of Nonmonotonic 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
  • Chapter 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
  • Chapter 10. Conclusions, Open Questions and Final Remarks
  • 10.1 Summary
  • 10.2 Future Work and Outlook
  • Bibliography

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