Lecture 1: Introduction
Slides
Course logistics, a brief history of AI, and the PEAS
model for understanding rational agents, which will help us conceptually organize the various
problems we will tackle in the class.
Book chapters: Ch. 1 (Introduction), Ch. 2 (Intelligent Agents)
Note: The book covers much more than we are able to see in class. Unless otherwise specified, the slides should be self-contained for you to follow the course, and the book is a resource for going deeper into material that interests you.
Recap of the PEAS model
and definition of search problems as a kind of static, deterministic, discrete environment;
Uninformed Search methods (BFS, DFS, ID-DFS, UCS), Informed Search and heuristics (A* and variants),
admissible heuristics and optimality, deriving heuristics from relaxations.
Live coding: [Github]
Book chapters: Ch. 3 (Solving Problems via Search)
Optional additional readings:
Two papers using learned search heuristics: DeepCubeA (Rubik's Cube), BFS-Prover (theorem proving in Lean 4)
Lecture 3: Constraint Satisfaction
Slides
Definition of a CSP (Constraint Satisfaction Problem). Sudoku as a CSP. Our first factored state representation: states are now variable assignments.
Backtracking search and heuristics for variable and value ordering (LCV, MRV), and inference (AC-3, forward checking, MAC).
Book chapters: Ch. 6 (Constraint Satisfaction Problems)
Optional additional reading:
Training a Transformer to solve CSPs via local search: ConsFormer (ICML 2025)
Lecture 4: Propositional Logic
Slides
Knowledge-Based Agents. Propositional Logic syntax and semantics. Truth-table method for deciding entailment. Satisfiability and validity. Inference rules, soundness. Proofs, completeness, refutation-completeness and resolution.
Book chapters: Ch. 7 (Logical Agents)
Optional additional reading:
U-M Prof. Karem Sakallah and former PhD student João Marques-Silva just won an award from the SAT association for 30 years of their groundbreaking work on SAT solving:
CSE article