My sparse literature notes of the Artificial Intelligence: A Modern Approach book by Peter Norvig and Stuart Russel.
This try chapters answer the question of what exactly is AI
Agents and task environments' characteristics.
Q: Components of an "agent problem"? A: PEAS
The classical searching algorithms that you would see in typical intro algorithms type of books.
What are the Local search methods mentioned in this chapter?
What search algorithm could be use in nondeterministic environments?:: AND-OR search.
What is the plan that AND-OR search generate?:: contigent plans.
When the environment is partially observable, what state is kept tracked of?:: belief state.
Getting to know a new type and closer to being useful in real world class of problems.
How would you do search in competitive environments / games.
What are the formulation components that a game is defined by? ?
What do you call the characteristic the environment the state is always transparent?:: perfect information.
What is the algorithm to you calculate the best move for you, the worst move for the opponents, turn by turn?:: Minmax algorithm
--SR:!2022-01-06,2,246-->
What is the more optimized version of Minmax algorithm?:: Alpha-beta algorithm.
What optimization does Alpha-beta algorithm do over Minmax algorithm?:: It eliminates subtrees that are provably irrelevant.
In situations where it is still expensive even with Alpha-beta algorithm, what can you do?:: cut the search off at some point, and estimate the utility of the states with an heuristic evaluation function.
"Sometimes you can trade space for time", know what this allures to?:: Precomputed tables for opening and endgame.