The document discusses problem-solving in artificial intelligence through state space search and control strategies, emphasizing the systematic examination of states to find a path from the initial to the goal state. It covers various search algorithms, including heuristic and uninformed search methods, along with the concepts of search space, initial and goal states, and path costs. Overall, it outlines how actions change states within a structured framework to achieve desired outcomes.

1. LEARN A.I. WITH
SHANKAR

2. Problem Solving: State Space Search
and Control Strategies in Artificial
Intelligence
SEARCH SPACE STRATEGIES IN ARTIFICIAL
INTELLIGENCE

3. SEARCH TREE
 Search is the systematic examination of
states to find path from the start/root state
to the goal state.
 To arrive at the best answer.
 It’s algorithm output is a solution, i.e., a
path from the initial state to a state that
satisfies the goal test.

4. Example

5. Search Algorithms
Heuristic algorithms are not really
intelligent.
Heuristic algorithms are more
efficient.
Uninformed search.
Informed search

6. Hierarchical Representation of Search Algorithms

7. Search Space
 A set of all states which can be reached, constitute a
search space.

8. Formal Statement
 Problem solving is a set of statements
describing the desired states expressed in a
suitable language.
 Initial (start) state to a final (goal) state.
 Each action changes the state.

9. Example
 Initial State: (S)
 Operator or successor function : for any state
x , returns s(x)
 State space : from initial by any sequence of
actions.
 Path : sequence through state space.
 Path cost : function that assigns a cost to a
path
 Goal state : (G)