Introduction about the Hill-climbing search algorithm

1. Introduction
HillHill
climbingclimbing

2. Artificial Intelligence search
algorithms
Search techniques are general problem-solving methods. When
there is a formulated search problem, a set of states, a set of
operators, an initial state, and a goal criterion we can use search
techniques to solve the problem (Pearl & Korf, 1987)

3. Search Methods
Brute-Force / Blind Search Methods
Breadth-First Search
Depth-First Search
DFS Iterative Deepening (DFID)
Iterative Broadening
British Museum

4. Search Methods
Heuristic Search
Hill Climbing
Steepest Ascent
Branch and Bound
Best-First Search
Beam Search
A*
Iterative-Deepening A*
B'
Simulated Annealing

5. Hill Climbing
Looking at all of our operators, we see which one,
when applied, leads to a state closest to the goal.
We then apply that operator. The process repeats
until no operator can improve our current situation
(which may be a relative maximum, such as in the
TSP).

6. Hill Climbing
Problems with hill climbing: local maxima (we've
climbed to the top of the hill, and missed the
mountain), plateau (everything around is about as
good as where we are), ridges (we're on a ridge
leading up, but we can't directly apply an operator
to improve our situation, so we have to apply
more than one operator to get there).

7. Hill Climbing
Solutions include: backtracking, making big jumps
(to handle plateaus or poor local maxima),
applying multiple rules before testing (helps with
ridges).

8. Hill-Climbing Methodology
●
Construct a sub-optimal solution that meets the
constraints of the problem
●
Take the solution and make an improvement
upon it
●
Repeatedly improve the solution until no more
improvements are necessary/possible