logical foundations of diagnosis in AI systems

Target entity: logical foundations of diagnosis in AI systems
Context triple: [Raymond Reiter, contributedTo, logical foundations of diagnosis in AI systems]

• A. Logical Methods in Computer Science
  Logical Methods in Computer Science is a peer-reviewed open-access journal focusing on theoretical computer science, particularly logic and its applications to computer science.
• B. Probabilistic Reasoning in Intelligent Systems
  Probabilistic Reasoning in Intelligent Systems is a foundational book by Judea Pearl that introduced Bayesian networks and revolutionized the use of probability theory for reasoning and decision-making in artificial intelligence.
• C. “A System for Representing and Using Real-World Knowledge”
  “A System for Representing and Using Real-World Knowledge” is a seminal AI research paper by John McCarthy that introduces a logical framework for representing commonsense knowledge about the real world.
• D. "Computer-Aided Reasoning: An Approach"
  "Computer-Aided Reasoning: An Approach" is a foundational book on automated and interactive theorem proving that presents methods and tools for using computers to assist in formal reasoning and proof development.
• E. Studies in the Logic of Confirmation
  "Studies in the Logic of Confirmation" is a seminal philosophical paper by Carl Gustav Hempel that analyzes how empirical evidence supports scientific hypotheses and introduces influential paradoxes about confirmation.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: logical foundations of diagnosis in AI systems
Target entity description: The logical foundations of diagnosis in AI systems is a theoretical framework that uses formal logic to model, explain, and infer the causes of system malfunctions or abnormal behaviors.

• A. Logical Methods in Computer Science
  Logical Methods in Computer Science is a peer-reviewed open-access journal focusing on theoretical computer science, particularly logic and its applications to computer science.
• B. Probabilistic Reasoning in Intelligent Systems
  Probabilistic Reasoning in Intelligent Systems is a foundational book by Judea Pearl that introduced Bayesian networks and revolutionized the use of probability theory for reasoning and decision-making in artificial intelligence.
• C. “A System for Representing and Using Real-World Knowledge”
  “A System for Representing and Using Real-World Knowledge” is a seminal AI research paper by John McCarthy that introduces a logical framework for representing commonsense knowledge about the real world.
• D. "Computer-Aided Reasoning: An Approach"
  "Computer-Aided Reasoning: An Approach" is a foundational book on automated and interactive theorem proving that presents methods and tools for using computers to assist in formal reasoning and proof development.
• E. Studies in the Logic of Confirmation
  "Studies in the Logic of Confirmation" is a seminal philosophical paper by Carl Gustav Hempel that analyzes how empirical evidence supports scientific hypotheses and introduces influential paradoxes about confirmation.
• F. None of above. chosen