## Learning Objectives

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, students will be able to:

- 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*). - Formulate simple reasoning, learning and optimization problems, in terms of the representations and methods presented (
*homework, quiz*). - 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, decision trees, linear programs and integer programs (
*homework, quiz*). - Demonstrate the hand execution of basic reasoning and optimization algorithms on simple problems (
*homework, quiz*). - Formulate more complex, but still relatively simple problems, and apply implementations of selected algorithms to solve these problems (
*homework, lab*). - Evaluate analytically the limitations of these algorithms, and assess tradeoffs between these algorithms (
*homework, quiz*).

## Concepts

Search and Reasoning: uninformed and informed search, game theory, stochastic search, constraint satisfaction, propositional inference, activity and motion planning and model-based diagnosis, Markov decision processes and hidden Markov models (HMM).

Optimization: Linear programming, integer programming, and finite domain constraint optimization,

Learning: reinforcement learning, conflict learning and HMM learning.