The document discusses artificial intelligence problem spaces and state space representation, outlining key concepts such as defining the problem, analyzing it, and selecting appropriate problem-solving techniques. It explains uninformed (blind) and informed (heuristic) search techniques, highlighting their differences in complexity, optimality, and methods like breadth-first and A* search. Additionally, it lists various AI problems related to games, routing, layout, assembly sequencing, and task scheduling.

1. ARTIFICIAL INTELLIGENCE
PART #4

2. ◊ Problem Spaces.
◊ State Space.
◊ Searching Techniques.
◊ Uninformed search technique.
◊ Informed search techniques.
◊ Some AI problems.
In This Video

3. Problem Space
Define the problem in detail is known as problem
space. There are 4 things concerned to solve a
particular problem.
 Define the problem precisely.
 Analyze the problem.
 Isolate and represent the task knowledge i.e.
required to solve the problem.
 Selection of the best problem solving technique
and apply it. Like Tower of Hanoi, 8 puzzle games.

4.  Set of states and the
connections between
them to represent the
problem.
 Graph is used represent
problem.
 States are represented
by nodes of the graph,
and the operators by
edges between nodes.
 For simplicity it is
represented as trees,
where the initial state
is the root of the tree.
State Space

5. S:{S,A,Action(s),Result(s,a),Cost(s,a)}
Where,
S: set of states like start state, goal state,
etc.
A :set of actions available.
s: an individual state from S.
Action(s): action performed on s .
a: any action from set A.
Result(s, a): An intermediate state obtain
by performing a on s.
Cost(s, a):cost of executing action a on s.
branching factor, should be minimum.
State Space Search

6. Problem Space of 8 puzzle

7. Search Strategy
A strategy is defined by picking the order of node
expansion.
 Completeness- does it always find a solution if
one exists?
 Time complexity- no. of nodes generated.
 Space complexity- max no. of nodes in memory.
 Optimality- does it always find least cost
solution?
Time and space is measured in
‘b’ – max branching factor of search tree.
‘d’- depth of least cost solution.
‘m’ – max depth of the state space may be
(infinite)

8. Searching
Techniques
Uninformed
/Blind search
Informed
/Heuristic search
Searching Techniques

9. Uninformed Search
 Also known as Blind search.
 Problems require no domain specific knowledge.
 No idea, how to obtain solution, we apply Brute
force technique (explore every possible step).
 Time consuming.
 Time/Space Complexity is more.
For e.g., Travelling salesman problem for 5 cities
needs (n-1)! Search.
 Non polynomial method.
 Provides optimal solution.
 Methods: Breath first search (BFS), Depth first
search(DFS)

10. Informed Search
• Also known as Heuristic
search(estimation).
• Search with information.
• Heuristic function is used to find steps of
the solution.
• Provides quick solution.
• Less Time and space complexity.
• Does not produce optimal solution.
• Perform search in exponential time.
• Methods: A*, Heuristic DFS, Best first
search.

11. AI PROBLEMS
• Games:Water Jug problem ,8 Queen problem, 8
Puzzle problem
• Missionaries and Cannibals ,The Monday &
Bananas problem.
• Route finding.(computer networks, airline
travel planning system)
• Layout problems (VLSI layout, furniture
layout, packaging)
• Assembly sequencing.(Assembly of electrical
motors)
• Task Scheduling.(Time tables, manufacturing)