Schedule
This schedule is tentative and subject to change. It may be a bit ambitious.
Supplmentary and optional background reading provided when appropriate.
R&N == Aritificial Intelligence: A Modern Approach, Russell and Norvig, 3rd edition.
References for Unit 1 (Logic)
For the unit on logic you may also want to consult:
 Category Theory for the Sciences, Spivak, pdf
 Logic in Computer Science, Huth and Ryan
 Logic for Computer Scientists, Schoening
Huth and Ryan is an excellent introductory text for temporal and epistemic logics, which we will touch on in Unit 3 (agentbased reasoning).
References for Unit 3 (Agents and Probabilistic State)
 Advanced Data Analysis from an Elementary Point of View, Part III, Shalizi, pdf
 Logic in Computer Science, Huth and Ryan
References for Unit 4 (Time and Programs)
 Logic in Computer Science, Huth and Ryan
 Program Synthesis by Sketching, Solar Lezama
 Course on Program Synthesis, Solar Lezama
 Program Synthesis with Types, Osera
Date  Topic  Form  Deadlines & Notes 

Wed, Jan 19  Intro: What is AI?  Lecture  
Fri, Jan 21  Knowledge Representation  Lecture  R&N: 12.1, 12.2 Set notation cheatsheet NSF Workshop: Research Challenges and Opportunitites in KR 
Mon, Jan 24  Propositional Logic  Lecture  Add Deadline R&N: 7.3 
Wed, Jan 26  First Order (Predicate) Logic  Lecture  Theory Assignment 1 out R&N 8.2 
Fri, Jan 28  Logical Inference I  Lecture  R&N 7.5 
Mon, Jan 31  Review of Logical Inference  Michael Q&A  No Teams broadcast today Programming Assignment 1 out 
Wed, Feb 2  Logical Inference II and Resolution  Videos  Theory Assignment 1 due (soft) R&N 7.5, 9.5 
Fri, Feb 4  Application: Law and Logic Programming  Lecture  Drop Deadline Theory Assignment 1 due (hard) R&N 9.4 
Mon, Feb 7  Proofs as Planning and Intro to Search  Lecture  R&N 10.1, 10.2 
Wed, Feb 9  Exam 1: Logic  Exam  
Fri, Feb 11  Background: Discrete Probability Theory  Lecture  Programming Assignment 1 due (soft) 
Mon, Feb 14  CANCELLED  
Wed, Feb 16  CANCELLED  
Fri, Feb 18  Search Agents  Lecture (Michael)  R&N 2.14 
Mon, Feb 21  President's Day  No Class  
Wed, Feb 23  Uninformed Search  Lecture (Michael)  R&N 3.14 
Fri, Feb 25  InClass Activity: Search  Lecture (Michael)  
Mon, Feb 28  A* and Adversarial Search  Lecture (Michael)  R&N 3.56, 5.13 
Wed, Mar 2  Constraint Satisfaction Problems  Lecture (Michael)  R&N 6.15 
Fri, Mar 4  InClass Actibity: CSP  Lecture (Michael)  
Mon, Mar 7  Spring Recess  No Class  
Wed, Mar 9  Spring Recess  No Class  
Fri, Mar 11  Spring Recess  No Class  
Mon, Mar 14  AI Security Topics  Lecture (Michael)  
Wed, Mar 16  Uncertainty in States  Lecture  R&N 12.14 
Fri, Mar 18  Queries and Partial Observability  Lecture  R&N 13.12 We briefly discussed what a naive causal structure learning algorithm would look like. For a full treatment of constraintbased causal structure learning, see Shalizi Ch. 25 
Mon, Mar 21  Causal Graphical Models  Lecture  Notebook exercises Actual notebook 
Wed, Mar 23  Modal Logics for Knowledge and Belief  Lecture  Modal Logic Playground DSL for belief programming with partial observability 
Fri, Mar 25  Representing agent knowledge with \(KT45^n\)  Lecture  An Introduction to Logics of Knowledge and Belief ICAPS 2020 Tutorial on Epistemic Planning 
Mon, Mar 28  Elementary Decision Theory  Lecture  Blog post 
Wed, Mar 30  Elementary Game Theory  Lecture  R&N 17.5, 17.6 Epistemic Game Theory Twoperson Zerosum Games Note that in this document, Player 1 chooses a row, whereas our Player 1 chooses a column 
Fri, Apr 1  Summary: Acting under incomplete or uncertain knowledge  Lecture  Probabilistic Modal Logic Factored Models for Probabilistic Modal Logic 
Mon, Apr 4  Temporal Logic for Representing Transitions  Lecture  Last day to Withdraw R&N 14, 17 
Wed, Apr 6  Exam Review  Review  
Fri, Apr 8  Exam 3: Agentbased Reasoning  Exam  
Mon, Apr 11  Exam 3: Agentbased Reasoning  MakeUp Exam  
Wed, Apr 13  Probabilistic Modeling Review  Inclass Work  Probabilistic Modeling Worksheet out 
Fri, Apr 15  Exam and programming assignment review  Inclass Unit 3 review  Programming Assignment 2 out 
Mon, Apr 18  LTL Applications  Lecture  
Wed, Apr 20  Markov Chains for Representing Transitions  Lecture  
Fri, Apr 22  Properties of Markov Chains and MDPs  Lecture  Probabilistic Modeling worksheet due in class Irreducibility and Aperiodicity Demo 
Mon, Apr 25  HMMs, MDPs, Intro to RL  Lecture  Probabilistic Modeling worksheet solutions out Real Applications of MDPs 
Wed, Apr 27  Lecture  
Fri, Apr 29  Lecture  Programming Assignment 2 due Programming Assignment 3 out 

Mon, May 2  Lecture  VerifAI: A Toolkit for the Formal Design and Analysis of Artificial IntelligenceBased Systems  
Wed, May 4  Exam Review  Review  
Fri, May 6  Exam 4: Time and Programs  Exam  Last Day of Classes 
Thu, May 12  Final Exam  7:30am10:15, VOTEY 207  All programming assignments due (hard) 