This course is about the foundations of theory and practice of
Artificial Intelligence. But, what AI means/is?
A number of different definitions of AI have been given so far. In general, all
include the underlying idea of equipping machines (e.g., computer programs,
robots) with "tools" to display behaviors that we, as humans, would be inclined
to categorize as "intelligent". This is still quite vague, since it
requires to clarify in turn what an intelligent behavior is.
In this course, this is issue is somehow narrowed down to the concept of rationality : the notion of Artificial Intelligence is treated as equivalent to that of Computational Rationality. Given well defined preferences, a rational agent always selects the actions whose outcomes result in the maximization of the expected utility.
The course will provide an overview of modern techniques for rational decisionmaking under different conditions regarding available information, certainty or uncertainty of the information, presence of one or more agents in both cooperative and adversarial scenarios.
More precisely, during the course, fundamental questions about the design of AI systems will be addressed, such as:
how to represent knowledge,
how to effectively generate provably optimal sequences of actions, (i.e., how to make optimal decision plans or define decision policies);
how to search among (large sets of) alternatives to find optimal or nearoptimal solutions;
how to learn to make optimal decisions.
Both deterministic and stochastic environments will be considered and treated separately.
Machine learning techniques will be introduced for dealing with supervised classification and regression tasks. Specialized deep learning techniques will be also considered, for image classification tasks. Reinforcement learning will be studied for sequential, interactive decisionmaking in unknown environments. Decisionmaking in multiagent systems will be also investigated, considering multiple agents with conflicting goals (known as games) under different conditions and available information.
There are three primary objectives for the course:
To provide a broad survey of AI and of the most popular techniques that are employed for: knowledge representation, problem solving, mathematical optimization, automated planning, probabilistic prediction and inference, sequential decisionmaking, supervised and reinforcement learning, deep learning, decisionmaking in multiagent adversarial scenarios.
To develop a thorough understanding of the algorithmic foundations of AI and acquire a strong appreciation of the bigpicture aspects of designing fully autonomous intelligent agents.
To develop operational knownhow about how to build/program AI agents, and analyze and improve their performance.
The course will be based on lectures. Students are expected to attend all classes and to actively participate with questions. Each lecture will feature a number of Quizzes to let the students engaged and the instructor check the understanding of the contents from the students as they are delivered.
For each one of the different topics, the course will present relevant techniques, discuss formal results, and show the application to problems of practical and theoretical interest. Weekly recitations will be used to practice together with the algorithms and their application to different problems.
Homework will be assigned (approximately) weekly. Each homework will include both questions to be answered and programming assignments. Written questions will involve working through different algorithms, deriving and proving mathematical results, and critically analyzing the material presented in class. Programming assignments will mainly involve writing code in Python to implement and test algorithms.
During the last four weeks, the students will work on a project that will let them experiencing with and comparing the different approaches presented during the course on a challenging scenario of practical interest.
Since the course will cover a number of different topics, students should have some background in programming (in Pyhton), algorithms, calculus, linear algebra, and probability theory. Please talk to the instructor if you are unsure whether your background is suitable for the course.
The formal prequisites for this course are:
The corequisite for this course is:
For this corequisite, you should either have completed it prior to starting 15281 or have it on your schedule for Spring 2020.
Grading will assigned based on the following weighting: 40% Homework, 30% Final exam, 10% Midterm exam, 20% Project. The final exam will include questions about all topics considered in the course, with an emphasis on the topics introduced after the midterm exam.
There is no required textbook for this class. The students should be able to learn everything from lecture handouts and homework. However, the Russel and Norvig textbook will cover good parts of the course. During the course, other books will be pointed out by the instructor to cover specific parts of the course.
Dates  Topic  Slides  References  

8/25  AI Problems and agents, Search problems: Taxonomy of problems and types of agents; planning and search problems; performance metrics; search metaalgorithms: Tree and Graph search; Informed vs. Uninformed search  R&N Ch. 3  
8/26  Uniformed search: BFS, UCS, DFS, DLDFS, IDS  R&N Ch. 3  


9/1  Informed Search for Path planning problems:  Theta* paper at AAAI07  
9/2  Recitation: Search algorithms  


9/8  Adversarial search 2: Adversarial search under uncertainty; role of chance; probabilities, expected values, and expectimax search; algorithm and properties; (no) pruning.  R&N Ch. 5  
9/9  Recitation: Adversarial search  


9/15  Classical planning 2: Planning graphs, GRAPHPLAN, Relaxed plans, Domainindependent heuristics  R&N Ch. 10  
9/16  Recitation: Classical planning   


9/22  Constraint Satisfaction Problems (CSPs) 2, Local search: CSP: Inferencepropagation
methods; Local search and local search methods for CSP; hillclimbing; WalkSAT 
R&N Ch. 4, 6 Paper on WalkSAT (Local search for maxSAT) in DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26, 1996 

9/23  Recitation: CSPs  


9/29  Optimization models 2, Integer optimization: Integer optimization problems; properties, modeling and solution approaches; branch and bound  BranchandBound, book chapter  
9/30  Recitation: Optimization models and methods  


10/6  Random processes: Prediction and analysis of random processes; Markov property; taxonomy of Markov Processes; rewards and policies. Markov chains, general properties and formalism; limiting and Stationary distributions in Markov Chains; state classification  R&N Ch. 15 S. Ross, Introduction to probability models, Ch. 4 

10/7  Recitation: Probabilities and random processes  


10/12  Fall break  
10/13  Fall break  
10/14  Fall break  


10/20  Bayesian Networks 2: Exact inference, Samplingbased methods for inference  R&N Ch. 13, 14  
10/21  Recitation: Bayesian networks  


10/27  Hidden Markov Models (HMM) 2: Robot localization problems; Particle filtering, sampling and resampling; application to SLAM problems.  R&N 15.2  15.6  
10/28  Recitation: HMM  


11/3  Markov Decision Processes 2: Value Iteration, Measures of convergence of Value Iteration, Variants of Value Iteration, Policy Iteration  BellmanFord algorithm, deterministic dynamic programming, asynchronous, distributed  
11/4  Recitation: MDPs  


11/10  Reinforcement Learning 2: Active RL, Exploration vs. exploitation, Monte Carlo action learning, SARSA, QLearning  Diagram summarizing the topics investigated in the RL lectures  
11/11  Recitation: RL  


11/17  Reinforcement Learning 4: Approximate methods for online policy control; policy gradient methods; deep reinformement learning  
11/18  Recitation: RL and approximation  


11/24  Game Theory 2: Mixed strategies, computing Nash equilibria, game formalization and analysis using algebra and calculus  
11/25  Recitation: Game Theory  


12/1  Game Theory 4: Social choice, voting theory  
12/2  Ethics and AI  

Topic  Files  Due Dates  

HW 2: Informed Search  
HW 3: Adversarial Search  
HW 4: Classical planning  
HW 5: Constraint Satisfaction Problems  
HW 6: Modeling and solving optimization problems  
HW 7: Probabilistic modeling, Markov chains  
HW 8: Bayesian networks  
HW 9: Hidden Markov Models  
HW 10: Markov Decision Processes for sequentialdecision making  
HW 11: Reinforcement learning with tabular methods  
HW 12: Reinforcement learning with approximation methods  
HW 13: Game Theory for multiagent adversarial scenarios  

Homework is due on Autolab by the posted deadline. Assignments submitted past the deadline will incur the use of late days.
You have 6 late days, but cannot use more than 2 late days per homework. No credit will be given for homework submitted more than 2 days after the due date. After your 6 late days have been used you will receive 20% off for each additional day late.
You can discuss the exercises with your classmates, but you should write up your own solutions. If you find a solution in any source other than the material provided on the course website or the textbook, you must mention the source. You can work on the programming questions in pairs, but theoretical questions are always submitted individually. Make sure that you include a README file with your andrew id and your collaborator's andrew id.
In general, for both assignments and exams, CMU's directives for academic integrity apply and must be duly followed. Students are required to refer to CMU's general policies on cheating and plagiarism: https://www.cmu.edu/policies/studentandstudentlife/academicintegrity.html
The class includes both a midterm and a final exam. The midterm is set for TBD and the final for TBD.
During exams students are allowed to consult 1page cheatsheet
(written in any desired format). No other material is allowed, including
textbooks, computers/smartphones, or copies of lecture handouts.
Students with special needs or disabilities are kindly asked to talk about it to the teacher at the beginning of the semester and make use of the assistance procedures provided by CMU https://scotty.qatar.cmu.edu/qword/studentaffairs/officeofhealthandwellness/assistanceforindividualswithdisabilities
Name  Hours  Location  

Gianni Di Caro  gdicaro@cmu.edu  Thursdays 1:302:30pm + Drop in at my office any time ...  M 1007 