Hill climbing is an optimization technique that iteratively improves the current state by evaluating possible successor states. It works by considering states laid out on a landscape, where the height corresponds to the evaluation function - it can only move to higher points. A problem is that it can get stuck at local maxima. Variants like simulated annealing allow worse states to be taken temporarily to avoid dead ends. Hill climbing has been applied to problems like the travelling salesman problem and in areas like robotics coordination and machine learning.

1. HILL CLIMBING
Search algorithm

2. AGENDA
1. Optimization technique
2. Iterative Improvement
3. Algorithm in Pseudocode
4. Applications

3. OPTIMIZATIONTECHNIQUE
Hill climbing is an optimization technique for solving computationally hard
problems. It is best used in problems with “the property that the state
description itself contains all the information needed for a solution” (Russell
& Norvig, 2003).[1] The algorithm is memory efficient since it does not
maintain a search tree: It looks only at the current state and immediate
future states.

4. OPTIMIZATIONTECHNIQUE
Hill climbing attempts to iteratively improve the current state by means of
an evaluation function. “Consider all the [possible] states laid out on the
surface of a landscape.The height of any point on the landscape
corresponds to the evaluation function of the state at that point” (Russell &
Norvig, 2003).[1]

5. OPTIMIZATIONTECHNIQUE
In contrast with other iterative improvement algorithms, hill-climbing
always attempts to make changes that improve the current state. In other
words, hill-climbing can only advance if there is a higher point in the
adjacent landscape.

6. ITERATIVE IMPROVEMENT
The main problem that hill climbing can encounter is that of local maxima.
This occurs when the algorithm stops making progress towards an optimal
solution; mainly due to the lack of immediate improvement in adjacent
states.

7. ITERATIVE IMPROVEMENT
Local maxima can be avoided by a variety of methods: Simulated annealing
tackles this issue by allowing some steps to be taken which decrease the
immediate optimality of the current state. Algorithms such as simulated
annealing “can sometimes make changes that make things worse, at least
temporarily” (Russell & Norvig, 2003).[1] This allows for the avoidance of
dead ends in the search path.

8. ALGORITHM IN PSEUDOCODE

9. function HILL-CLIMBING(problem) returns a solution state
inputs: problem, a problem
static: current, a node
next, a node
current <— MAKE-NODE(lNlTIAL-STATE[problem])
loop do
next— a highest-valued successor of current
ifVALUE[next] <VALUE[current] then return current
current *—next
end

10. APPLICATIONS
Hill climbing can be applied to any problem where the current state allows
for an accurate evaluation function. For example, the travelling salesman
problem, the eight-queens problem, circuit design, and a variety of other
real-world problems. Hill Climbing has been used in inductive learning
models. One such example is PALO, a probabilistic hill climbing system
which models inductive and speed-up learning. Some applications of this
system have been fit into “explanation-based learning systems”, and “utility
analysis” models. (Cohen, Greiner, & Schuurmans, 1994)

11. APPLICATIONS
Hill Climbing has also been used in robotics to manage multiple-robot
teams. One such example is the Parish algorithm, which allows for scalable
and efficient coordination in multi-robot systems.The group of researchers
designed “a team of robots [that] must coordinate their actions so as to
guarantee location of a skilled evader.” (Gerkey,Thrun, & Gordon, 2005).[3]

12. APPLICATIONS
Their algorithm allows robots to choose whether to work alone or in teams
by using hill-climbing. Robots executing Parish are therefore “collectively
hill-climbing according to local progress gradients, but stochastically make
lateral or downward moves to help the system escape from local maxima.”
(Gerkey,Thrun, & Gordon, 2005)

13. THANKYOU