This module, we will set us up for some key considerations we’ll make when designing our own AI systems and how they should behave. Should they act like humans do, or think like humans do, or act and think rationally? We'll define what rational agents are and explore task environments before completing our Python review. At the end of the module, you will work on your first of three programming assignments.

In artificial intelligence, a surprising number of tasks that we want to solve can be cast as search problems. This module, we will introduce the formal definition of search problems, and examine some classic algorithms for solving search problems called shortest path algorithms. These are sometimes referred to as “uninformed” search algorithms or “blind” search algorithms, because they are run without any additional knowledge of where our goal lies. We’ll also look at some variants of these algorithms that have computational complexity guarantees.

We can often find a solution to a search problem more quickly if we have some knowledge about how close we are to a goal state. This module, we’ll look at the process of incorporating such knowledge into search algorithms, which, when used optimally, can help focus our search efforts so that we avoid exploring actions that move us further away from the goal. We’ll examine the most famous informed search algorithm, A* search, which is guaranteed to find an optimal solution first.