Lecture slides (to appear here incrementally)

Slides 0: Introduction to CSC 244/444
Slides 1: Course overview; logic in AI
Slides 2: Representing knowledge in FOL
Slides 3: Semantics of FOL
Slides 4: Semantics, continued
Slides 5: Validity and Entailment
Slides 6: Provability, Soundness and Completeness
Slides 7: Resolution Principle Theorem Proving
Slides 8: Resolution Principle, Continued
Slides 9: Augmenting Resolution
Slides 10: Resolution Strategies
Slides 11: Natural Logic & EPILOG
Specialist Inference
SAT-Soving
We'll take only a brief look at production systems:
Production Systems (Expert Systems)
Frames
Inference with frames (highlighted text)
Nonmonotonic Reasoning
Nonmonotonic Inheritance Networks
We'll mostly skip over the following slides on default reasoning, except to indicate the format of default rules, and some of the pros & cons.
Default Reasoning
The following slides on probability and random variables are for review; you should know the probablity axioms, conditional probability, mutually exclusive (disjoint) events, independent events (and independent random variables), conditional independence, the chain rule, marginalization, and Bayes' rule & its use for inference.
Probability: A Quick Review
The following slides on (boolean) Bayesian networks are for review; you should know the form of the joint probability distribution of the boolean (Bernoulli) random variables contained in a BN, the conditional independence assumptions "built into" a BN, the particular relevance of BNs to causal relationships, what subnetwork is relevant to computing a desired conditional probability, how we can compute conditional probabilities by marginalizing over joint probabilities, and "Monte Carlo" (Gibbs sampling) methods of computing conditional probabilities.
Bayesian Networks: A Quick Review
Neural Net-Based Reasoning
Kassner et al., Languagen models with rationality
Planning and the Frame Problem
Planning from 2000 to present