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Prof. Brian Williams
Dr. Greg Sullivan

Course Meeting Times

Two sessions / week
1.5 hours / session

Course Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information technology, and graduate (16.413) students.

16.413 meets with undergraduate subject 16.410, but requires more advanced programming and written assignments, including a Mars Rover project.

Learning Objectives

Upon successful completion of 16.410 or 16.413, students will be able to:

  1. Model decision making problems using major modeling formalisms of artificial intelligence and operations research, including propositional logic, constraints, linear programs and Markov processes.

  2. Evaluate the computational performance of search, satisfaction, optimization and learning algorithms.

  3. Apply search, satisfaction, optimization and learning algorithms to real world problems.

Measurable Outcomes (Assessment Method)

Upon successful completion of 16.410 and 16.413, students will be able to:

  1. Describe at an intuitive level the process of artificial intelligence and operations research: a real-time cycle of problem understanding, formulation, solution and implementation (homework).

  2. Formulate simple reasoning, learning and optimization problems, in terms of the representations and methods presented (homework, quiz).

  3. Manipulate the basic mathematical structures underlying these methods, such as system state, search trees, plan spaces, model theory, propositional logic, constraint systems, Markov decision processes, value functions, policies, linear programs and integer programs (homework, quiz).

  4. Demonstrate the hand execution of basic reasoning and optimization algorithms on simple problems (homework, quiz).

  5. Formulate more complex, but still relatively simple problems, and apply implementations of selected algorithms to solve these problems (homework, lab).

  6. Evaluate analytically the limitations of these algorithms, and assess tradeoffs between these algorithms (homework, quiz).

In addition, upon successful completion of 16.413, students will be able to:

  1. Implement basic algoritms in reasoning, optimization and learning (homework, lab).

  2. Use model-based methods to autonomously operate spacecraft or rovers, either in simulation or in the laboratory (project).


Search and Reasoning: Uninformed and informed search, game tree search, local stochastic search and genetic algorithms, constraint satisfaction, propositional inference, rule-based systems, planning, and model-based diagnosis.
Optimization: Linear programming, integer programming, Markov decision processes.
Learning: Reinforcement learning.

Pedagogical Methods

  1. Computer projected presentations
  2. Chalk-talk
  3. Prepared notes handed out to class
  4. Text
  5. Web-based recitations
  6. Web-based problem sets
  7. Videos and computer demonstrations
  8. Computational lab in scheme
  9. Autonomous Mars rover laboratory


  • Russell, and Norvig. AI, A Modern Approach. Noted as AIMA. (On reserve at Barker Engineering Library.)
  • Hillier, and Lieberman. Introduction to Operations Research. (On reserve at Barker Engineering Library.)
  • Additional handouts.


  • Weekly, Due on Monday, unless otherwise indicated.
  • Web-based assignments due by midnight of date assigned.
  • Paper assignments due by 5pm to course secretary.


  • Coding exercises in MIT Scheme (a dialect of Lisp) and AMPL/MATLAB®
  • Computer lab

Evaluation: 16.410

  • Mid Term (30pts), Final (40 pts), Homework/Participation (30 pts)
  • Grade = Mid Term + Final + Homework/Participation

Evaluation: 16.413

  • 16.413 Mid Term (30pts), Final (40pts), Project (30pts)
  • Homework and class participation [0-1]
  • Grade = (Mid-term + Final + Project) * (log homework and class participation)


6.041 and 16.070

3-0-9 16.410 is U-LEVEL, 16.413 is H-LEVEL

The course textbook is Artificial Intelligence: A Modern Approach, 2nd Edition, by Stuart Russell and Peter Norvig. 

A recommended textbook is Introduction to Operations Research, by Frederick S. Hillier and Gerald J. Lieberman.