Classical and Modern Planning - Artificial Intelligence - Lecture Slides, Slides of Artificial Intelligence

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Lecture 24 of 41

Review: Classical and Modern Planning

Lecture Outline

• Today’s Reading
• Next Week: Chapter 14, Russell and Norvig 2e
• Previously: Logical Representations
• Today and Wednesday: Introduction to Reasoning under Uncertainty
  • Conditional planning, concluded
  • Monitoring
• Friday and Next Week: Introduction to Uncertain Reasoning
  • Uncertainty in AI
    • Need for uncertain representation
    • Soft computing: probabilistic, neural, fuzzy, other representations
  • Probabilistic knowledge representation
    • Views of probability
    • Justification

Review:

How Things Go Wrong

Example:

Preconditions for Remaining Plan

Example:

Replanning

Methods for Handling Uncertainty

Probability

Terminology

• Introduction to Reasoning under Uncertainty
  • Probability foundations
  • Definitions: subjectivist, frequentist, logicist
  • (3) Kolmogorov axioms
• Bayes’s Theorem
  • Prior probability of an event
  • Joint probability of an event
  • Conditional (posterior) probability of an event
• Maximum A Posteriori (MAP) and Maximum Likelihood (ML) Hypotheses
  • MAP hypothesis: highest conditional probability given observations (data)
  • ML: highest likelihood of generating the observed data
  • ML estimation (MLE): estimating parameters to find ML hypothesis
• Bayesian Inference: Computing Conditional Probabilities (CPs) in A Model
• Bayesian Learning: Searching Model (Hypothesis) Space using CPs

Summary Points

• Introduction to Probabilistic Reasoning
  • Framework: using probabilistic criteria to search H
  • Probability foundations
    • Definitions: subjectivist, objectivist; Bayesian, frequentist, logicist
    • Kolmogorov axioms
• Bayes’s Theorem
  • Definition of conditional (posterior) probability
  • Product rule
• Maximum A Posteriori (MAP) and Maximum Likelihood (ML) Hypotheses
  • Bayes’s Rule and MAP
  • Uniform priors: allow use of MLE to generate MAP hypotheses
  • Relation to version spaces, candidate elimination
• Next Week: Chapter 14, Russell and Norvig
  • Later: Bayesian learning: MDL, BOC, Gibbs, Simple (Naïve) Bayes
  • Categorizing text and documents, other applications