Combinatorial optimization problems consist of making a set of discrete but inter-related decisions to optimize some objective function. While such problems are economically important across many industries and services, they also grow exponentially in difficulty with problem size. Thus there exists a substantial literature on the theory and practice of combinatorial problem solving with Artificial Intelligence (AI) and Operations Research (OR).

This course will provide students with advanced conceptual, theoretical, and implementational knowledge and skills for modeling and solving such problems. The course will cover the fundamental components of developing mathematical models within existing AI frameworks of SAT, Constraint Programming, AI planning, and Domain-Independent Dynamic Programming while also teaching the fundamental mathematics and algorithms with which the frameworks solve the problem thus modeled.

Basic knowledge of symbolic AI approaches as typically taught in undergraduate courses, as well as a familiarity with computational complexity, is recommended. Knowledge of Operations Research approaches to combinatorial optimization is an asset.