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, Ma