03 Search I

How to search when we do not know how far is the goal. What does it mean that an algorithm is complete, optimal?

• discussion on the first assignment
• AI-gym works for everyone?
• Quiz

Let us start with a simple graph to practice searching. It is easier to debug and understand. The lecture example you can try out here: ''kuigraphs.py''.

The basic communication is the same as for kuimaze. I.e.:

import kuigraphs
env = kuigraphs.KuiGraph()
observation = env.reset()
states_with_costs = env.expand(state) # list of tuples ('a',1)
env.set_path(path)

state is a letter like in the lecture: 'S', 'a', … 'G'. Try out some search strategies. If you write them generally enough, your code will work also in the following case:

env = kuimaze.InfEasyMaze()

Think a little bit about the types of data structures you will need.

In the environment from Search program an algorithm that will find the cheapest path. Do not forget that changing positions does not have to cost the same in every case.