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 decision-making 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 near-optimal solutions;
how to learn to make optimal decisions.
Both deterministic and stochastic environments will be considered and treated separately.
Learning techniques will be introduced for dealing with supervised classification and regression tasks, as well as with online reinforcement-based action learning.
Decision-making in multi-agent systems will be also investigated, considering: two (or more) agents with conflicting goals (known as games), large numbers of relatively simple and self-organizing agents (swarm intelligence).
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, multi-step decision-making, supervised and reinforcement learning, adversarial search (games), swarm systems.
To develop a thorough understanding of the algorithmic foundations of AI and acquire a strong appreciation of the big-picture aspects of designing fully autonomous intelligent agents.
To develop operational known-how 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. The instructor will always be available at the end of each lecture for further clarifications and explanations.
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.
Homework will be assigned (approximately) by-weekly, for a total of 9 assignments, that 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.
The only strict pre-requisite is 15-122 (Principles of imperative programming). However, since the course will cover a number of different topics, students should have previous programming experience (programming assignments will be given in Python), as well as a solid general CS background, calculus, and basics of probability theory. Please talk to the instructor if you are unsure whether your background is suitable or not for the course.
Grading will assigned based on the following weighting: 40% Homework, 35% Final exam, 15% Midterm exam, 10% Participation. The final exam will include questions about all topics considered in the course, with an emphasis on the topics introduced after the midterm exam.
10% of the final grading will be based on attendance and participation. However, if a student only attends between 65% and 75% of the classes, his/her final grade will not be higher than B. For any attendance below 65%, the final grade will not be higher than C.
Of course, these restrictions will not apply in the case of major, certified, impediments.
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.
|8/21||Introduction, Definitions, Road map||R&N Ch. 1,2,26,27, Brief AI history, 2016 Stanford AI report|
|(Model-based) Automated Planning: Full information, Deterministic, Goal-based|
|8/23||Search problems, Uniformed search: BFS, DFS, UCS, IDS||R&N Ch. 3|
|8/28||Uninformed Search (continued) + Informed Search||R&N Ch. 3|
|8/30||A* properties, Theta*, path planning||R&N Ch. 3, Theta* paper at AAAI-07|
|9/4||Eid al-Adha break|
|9/6||Eid al-Adha break|
|9/11||STRIPS planning 1: Factored states, description language, heuristics||R&N Ch. 10|
|9/13||STRIPS planning 2: Planning graph, Relaxed plans, heuristics||R&N Ch. 10|
|Constraint Satisfaction and Optimization models and methods|
|9/18|| STRIPS planning 2a: GRAPHPLAN, backward search
Search in solution sets, Definition of Constraint Satisfaction Problems (CSPs)
| pdf (planning)
| R&N Ch. 10
R&N Ch. 6
|9/20|| Solution of Constraint Satisfaction Problems:
Backtracking, Arc consistency
|R&N Ch. 6|
|9/25||Optimization 1: Convex optimization problems|
|9/27||Optimization 2: Integer Programming models, Branch-and-bound methods|
|10/2||Optmization 3: IP solving, Local search algorithms|| pdf (IP solving)
pdf (Local search)
|10/4||Optimization 4: Local search, Iterated local search, Modeling with Markov chains|
|(Model-Based) Representation, Prediction, and Decision-Making under Uncertainty|
|10/9||Markov processes, Simulated Annealing|
|10/11||Markov Chains 1, prediction and analysis of random processes|
|10/18||Markov Chains 2, MC Monte Carlo for sampling according to a probability distribution||10/23||Markov Chains 3, Classification of states and chains||S. Ross, Introduction to probability models, Chapter 4|
|10/25||Egodic chains, From MCs to MDPs|
|10/30||Markov Decision Processes 1: Models, Utilities, Bellman equations||Musam and Kolobov, Planning with MDPs, Chapter 2|
|11/1||Markov Decision Processes 2: Value Iteration and variants, Policy Iteration||pdf (slides 39-50 will be explained next lecture)||Musam and Kolobov, Planning with MDPs, Chapter 3|
|Learning how to make decisions|
|11/6||Reinforcement Learning 1: Generalities, Taxonomy of Machine Learning approaches||RL I pdf
(MDP II pdf) (slides 39-50)
|11/8||Reinforcement Learning 2: Prediction / Policy evaluation with Monte Carlo and Temporal Differences (TD)||Sutton and Barto RL book, Nov'17 Draft|
|11/13||Reinforcement Learning 3: Control with SARSA and Q-Learning|
|11/15||Supervised Learning 1: Classification and Regression|
|11/20||Supervised Learning 2: Classification and Regression|
|Decision-making in multi-agent adversarial scenarios, imperfect information|
|11/22||Game Theory 1: Concepts, Nash equilibria|
|11/27||Game Theory 2: Mixed strategies, Correlated equilibria|
|11/29||Summary + Q&A|
|12/4||Final Exam (8:30 - 11:30, Room 2049)|
|Homework 1: Rationality, Uninformed and Informed Search||hw1.pdf, Viz.zip, GridWorlds.zip||Sep 13, 2017|
|Homework 2: STRIPS planning, CSPs||hw2.pdf, Graphplan.zip||Oct 5, 2017|
|Homework 3: Modeling and solving optimization problems||hw3.pdf, instances.zip||Oct 14, 2017|
|Homework 4: Application and analysis of Markov chains||hw4.pdf, hollins.dat||November 6, 2017|
|Homework 5: Markov decision processes, Reinforcement learning||hw5.pdf||November 26, 2017|
|Homework 6: Supervised Learning, Game Theory||hw6.pdf, poverty.dat||November 30, 2017|
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.
The class includes both a midterm and a final exam. The midterm is set for October 16 and Final is on December 4.
During exams students are allowed to consult 1-page cheatsheet
(written in any desired format). No other material is allowed, including
textbooks, computers/smartphones, or copies of lecture handouts.
|Gianni Di Carofirstname.lastname@example.org||Thursdays 2:30-3:30pm + Drop in at my office any time ...||M 1007|