Automated Reasoning - Logic, Probabilistic Reasoning and Machine Learning

Automated Reasoning - Logic, Probabilistic Reasoning and Machine Learning

UCLA Automated Reasoning Group via YouTube Direct link

Lecture 1A: Introduction & Boolean Logic

1 of 37

1 of 37

Lecture 1A: Introduction & Boolean Logic

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Automated Reasoning - Logic, Probabilistic Reasoning and Machine Learning

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  1. 1 Lecture 1A: Introduction & Boolean Logic
  2. 2 Lecture 1B: Boolean Logic Semantics
  3. 3 Lecture 2A: Quantified Boolean Logic & Resolution
  4. 4 Lecture 2B: Applications of Resolution
  5. 5 Lecture 3A: Directed Resolution
  6. 6 Lecture 3B: Directed Resolution & DPLL
  7. 7 Lecture 4A: DPLL & Modern SAT Solvers
  8. 8 Lecture 4B: Modern SAT Solvers
  9. 9 Lecture 5A: Exhaustive DPLL & Certifying UNSAT
  10. 10 Lecture 5B: More on SAT & Local Search
  11. 11 Lecture 6A: MAXSAT (Maximum Satisfiability)
  12. 12 Lecture 6B: MAXSAT Resolution & Beyond-NP Queries
  13. 13 Lecture 7A: Beyond NP
  14. 14 Lecture 7B: Tractable Circuits & Knowledge Compilation Map
  15. 15 Lecture 8A: DNNF Circuits (Decomposability)
  16. 16 Lecture 8B: DNNF Circuits (Minimization and Structured Decomposability)
  17. 17 Lecture 9A: d-DNNF circuits (Determinism and Smoothness)
  18. 18 Lecture 9B: Top-Down Knowledge Compilers
  19. 19 Lecture 10A: OBDD Circuits (Binary Decision Diagrams)
  20. 20 Lecture 10B: OBDD Circuits (Binary Decision Diagrams)
  21. 21 Lecture 11A: SDD Circuits (Sentential Decision Diagrams)
  22. 22 Lecture 11B: Bottom-Up Knowledge Compilers
  23. 23 Lecture 12A: PSDD Circuits (Probabilistic Sentential Decision Diagrams)
  24. 24 Lecture 12B: PSDD & Conditional PSDD Circuits
  25. 25 Lecture 13A: Prime Implicants and Implicates
  26. 26 Lecture 13B: Model-Based Diagnosis
  27. 27 Lecture 14A: Explaining Decisions (MC Explanations)
  28. 28 Lecture 14B: Explaining Decisions (PI Explanations, Sufficient & Complete Reasons)
  29. 29 On Boolean Quantification in Explainable AI | IJCAI-2022
  30. 30 On the Computation of Necessary and Sufficient Explanations | AAAI-2022
  31. 31 Lecture 15A: Compiling Bayesian Network Classifiers
  32. 32 Lecture 15B: Compiling Neural Network and Random Forest Classifiers
  33. 33 Lecture 16: Reducing Probabilistic Reasoning (MPE) to Weighted MAX-SAT
  34. 34 Lecture 17A: Reducing Probabilistic Reasoning (MAR) to Weighted Model Counting
  35. 35 Lecture 17B: Tractable Reasoning using Arithmetic Circuits (ACs)
  36. 36 Lecture 18A: Query-Oriented ACs, Tensor Graphs and Constrained SDDs
  37. 37 Lecture 18B: Width Parameters, Auxiliary Variables and Extended Resolution

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