AI3391 ARTIFICIAL INTELLIGENCE Department of Artificial intelligence and data science under anna university syllabus

1. AI3391 ARTIFICAL INTELLIGENCE
(II YEAR (III Sem))
Department of Artificial Intelligence and Data
Science
Session 11
by
Asst.Prof.M.Gokilavani
NIET
11/14/2023 Department of AI & DS 1

2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third
Edition, Stuart Russell and Peter Norvig, Pearson
Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight
(TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny
Winston, Pearson Education.
• Artificial Intelligence, Shivani Goel, Pearson
Education.
• Artificial Intelligence and Expert Systems- Patterson,
Pearson Education.
11/14/2023 Department of CSE (AI/ML) 2

3. Topics covered in session 11
11/14/2023 Department of AI & DS 3
Unit II: Problem Solving
• Heuristic search Strategies
• Heuristic function
• Local search and optimization problems
• Local search in continuous space
• Search with non deterministic actions
• Search in partial observation environments
• Online search agents and unknown environment

4. Beyond classical search
• We have seen methods that systematically
explore the search space, possibly using
principled pruning (e.g., A*)
What if we have much larger search spaces?
• Search spaces for some real-world problems
may be much larger e.g., 1030,000 states as in
certain reasoning and planning tasks.
• Some of these problems can be solved by
Iterative Improvement Methods
11/14/2023 4
Department of CSE (AI/ML)

5. Local Search Algorithms and
Optimization Problems
• In many optimization problems the goal state itself is the
solution.
• The state space is a set of complete configurations.
• Search is about finding the optimal configuration (as in
TSP) or just a feasible configuration (as in scheduling
problems).
• In such cases, one can use iterative improvement, or
local search, methods.
• An evaluation, or objective, function h must be available
that measures the quality of each state.
• Main Idea: Start with a random initial configuration and
make small, local changes to it that improve its quality.
11/14/2023 5
Department of CSE (AI/ML)

6. Hill Climbing Algorithm
• In Hill-Climbing technique, starting at the base of a
hill, we walk upwards until we reach the top of the
hill.
• In other words, we start with initial state and we keep
improving the solution until its optimal.
• It's a variation of a generate-and-test algorithm which
discards all states which do not look promising or
seem unlikely to lead us to the goal state.
• To take such decisions, it uses heuristics (an
evaluation function) which indicates how close the
current state is to the goal state.
• Hill-Climbing = generate-and-test + heuristics
11/14/2023 6
Department of CSE (AI/ML)

7. Features of Hill Climbing
Following are some main features of Hill Climbing
Algorithm:
• Generate and Test variant: Hill Climbing is the
variant of Generate and Test method. The Generate and
Test method produce feedback which helps to decide
which direction to move in the search space.
• Greedy approach: Hill-climbing algorithm search
moves in the direction which optimizes the cost.
• No backtracking: It does not backtrack the search
space, as it does not remember the previous states.
11/14/2023 Department of CSE (AI/ML) 7

8. Types of Hill Climbing Algorithm
• Simple hill Climbing
• Steepest-Ascent hill-climbing
• Stochastic hill Climbing
• Random-restart hill climbing
11/14/2023 Department of CSE (AI/ML) 8

9. Simple Hill Climbing
• Simple hill climbing is the simplest way to implement a
hill climbing algorithm.
• It only evaluates the neighbor node state at a time and
selects the first one which optimizes current cost and
set it as a current state.
• It only checks it's one successor state, and if it finds better
than the current state, then move else be in the same state.
• This algorithm has the following features:
• Less time consuming
• Less optimal solution and the solution is not
guaranteed
11/14/2023 Department of CSE (AI/ML) 9

10. Algorithm
• Define the current state as an initial state
• Loop until the goal state is achieved or no more operators
can be applied on the current state:
– Apply an operation to current state and get a new state
– Compare the new state with the goal
– Quit if the goal state is achieved
– Evaluate new state with heuristic function
and compare it with the current state
– If the newer state is closer to the goal compared to
current state, update the current state
• As we can see, it reaches the goal state with iterative
improvements. In Hill-Climbing algorithm, finding goal
is equivalent to reaching the top of the hill.
11/14/2023 10
Department of CSE (AI/ML)

11. Example
• Key point while solving any
hill-climbing problem is to
choose an
appropriate heuristic
function.
• Let's define such function h:
• h(x) = +1 for all the blocks
in the support structure if
the block is correctly
positioned otherwise -1 for
all the blocks in the
support structure.
11/14/2023 Department of CSE (AI/ML) 11

12. 11/14/2023 Department of CSE (AI/ML) 12

13. Steepest-ascent Hill climbing
• Steepest-ascent hill climbing is different from
simple hill climbing search.
• Unlike simple hill climbing search, It considers
all the successive nodes, compares them, and
choose the node which is closest to the solution.
• Steepest hill climbing search is similar to best-
first search because it focuses on each node
instead of one.
• Note: Both simple, as well as steepest-ascent hill
climbing search, fails when there is no closer
node.
11/14/2023 13
Department of CSE (AI/ML)

14. Algorithm
• Create a CURRENT node and a GOAL node.
• If the CURRENT node=GOAL node,
return GOAL and terminate the search.
• Loop until a better node is not found to reach
the solution.
• If there is any better successor node present,
expand it.
• When the GOAL is attained,
return GOAL and terminate.
11/14/2023 14
Department of CSE (AI/ML)

15. Stochastic hill climbing
• Stochastic hill climbing does not focus on all the nodes.
• It selects one node at random and decides whether it
should be expanded or search for a better one.
Random-restart hill climbing
• Random-restart algorithm is based on try and try
strategy.
• It iteratively searches the node and selects the best one
at each step until the goal is not found.
• The success depends most commonly on the shape of
the hill.
• If there are few plateaus, local maxima, and ridges, it
becomes easy to reach the destination.
11/14/2023 15
Department of CSE (AI/ML)

16. Simulated Annealing
11/14/2023 Department of CSE (AI/ML) 16
• A hill-climbing algorithm which never makes a move towards a
lower value guaranteed to be incomplete because it can get stuck on
a local maximum.
• And if algorithm applies a random walk, by moving a successor,
then it may complete but not efficient.
• Simulated Annealing is an algorithm which yields both efficiency
and completeness.
• In mechanical term Annealing is a process of hardening a metal or
glass to a high temperature then cooling gradually, so this allows the
metal to reach a low-energy crystalline state.
• The same process is used in simulated annealing in which the
algorithm picks a random move, instead of picking the best move.
• If the random move improves the state, then it follows the same
path.
• Otherwise, the algorithm follows the path which has a probability of
less than 1 or it moves downhill and chooses another path.

17. State-space Diagram for Hill Climbing
11/14/2023 Department of CSE (AI/ML) 17

18. • Local Maximum: Local maximum is a state
which is better than its neighbor states, but there
is also another state which is higher than it.
• Global Maximum: Global maximum is the best
possible state of state space landscape. It has the
highest value of objective function.
• Current state: It is a state in a landscape diagram
where an agent is currently present.
• Flat local maximum: It is a flat space in the
landscape where all the neighbor states of current
states have the same value.
• Shoulder: It is a plateau region which has an
uphill edge.
11/14/2023 18
Department of CSE (AI/ML)

19. Genetic Algorithm
11/14/2023 Department of CSE (AI/ML) 19

20. 11/14/2023 Department of CSE (AI/ML) 20

21. 11/14/2023 Department of CSE (AI/ML) 21

22. Topics to be covered in next session 12
• Local Search in Continuous Spaces
11/14/2023 Department of CSE (AI/ML) 22
Thank you!!!