2. Problem
Solving
Problem-solving refers to artificial
intelligence techniques, including
various techniques such as
forming efficient algorithms.
Heuristics, and performing root
cause analysis to find desirable
solutions.
The basic crux of artificial
intelligence is to solve problems
just like humans.
3. General Problem Solver
One of the very first attempts at AI was in 1956.
Allen Newell and Herbert A. Simon created a computer program they called
the
general problem solver.
This program was designed to solve any problem that could be presented in
the form of mathematical formulas.
5. Steps of Problem Solving in AI
Goal
Formulation
Problem
Formulation
6. Goal Formulation
The first and simple step in problem-solving.
It organizes finite steps to formulate a goals which require some action to
achieve the goal.
Today the formulation of the goal is based on AI agents.
7. Problem Formulation
It is one of the core steps of problem-solving which decides
what action should be taken to achieve the formulated goal.
In AI this core part is dependent upon software agent which consisted
components to formulate the associated problem.
9. Initial State
This state requires an initial state
for the problem which starts the AI
agent towards a specified goal.
10. Action
This stage of problem formulation
works with function with a specific
class taken from the initial state
and all possible actions done in this
stage.
11. Transition
Integrates the actual action
done by the previous action
stage and collects the final
stage to forward it to their
next stage.
12. Goal Test
The specified goal
achieved by the integrated
transition model or not,
whenever the goal achieves
stop the action and forward
into the next stage to
determines the cost to
achieve the goal.
13. Path Costing
Solving numerical assigned what will be
the cost to achieve the goal. It requires
all hardware software and human
working cost.
14. Types of Searching Algorithms
Based on the search problems, we can
classify the search algorithm as
•Uninformed search
•Informed search
15. Uninformed search
The uninformed search algorithm does not have any domain knowledge.
It behaves in a brute-force way.
It only knows the information about how to traverse the given tree.
And how to find the goal state.
This algorithm is also known as the Blind search algorithm or Brute -
Force algorithm.
18. Algorithm
A problem graph, containing the start node S and the goal node G.
A strategy, describing the manner in which the graph will be traversed to
get to G.
A fringe, which is a data structure used to store all the possible states
(nodes) that you can go from the current states.
19. Algorithm
A tree, that results while traversing to the goal node.
A solution plan, which the sequence of nodes from S to G.
20. Depth First Search
Depth-first search (DFS) is an
algorithm for searching tree.
The algorithm starts at the root node
(selecting some arbitrary node)
And explores as far as possible
along each branch before
backtracking.
21. Depth First Search
Which solution would DFS find
to move from node S to node G
if run on the graph below?
23. Breath First Search
Breadth-first search (BFS) is an algorithm
for searching tree.
It starts at the tree root (or some arbitrary
node of a graph).
And explores all the neighbor nodes at the
present depth prior to
moving on to the nodes at the next depth
level.
24. Breath First Search
Which solution would BFS
find to move from node S to
node G if run on the graph
below?
26. Informed Search Algorithm
The algorithms have information on the goal state.
Which helps in more efficient searching.
This information is obtained by something called a heuristic.
28. Search Heuristics
In an informed search, a heuristic is a function.
Task to estimates how close a state is to the goal state.
For example – Manhattan distance, Euclidean distance, etc.
(Lesser the distance, closer the goal)
29. Greedy Search
In greedy search, we expand the node closest to the goal node.
The “closeness” is estimated by a heuristic h(x).
Heuristic: A heuristic h is defined as-
h(x) = Estimate of distance of node x from the goal node.
Lower the value of h(x), closer is the node from the goal.
31. Greedy Search
Find the path from S to G using greedy search.
The heuristic values h of each node
below the name of the node.
32. Greedy Algorithm
Advantage:
Works well with informed search problems, with fewer steps
to reach a goal.
Disadvantage:
Can turn into unguided DFS in the worst case.
34. A* Tree Search
A* Tree Search, or simply known as A* Search.
Combines the strengths of uniform-cost search and greedy search.
The heuristic is the summation of the cost, denoted by g(x),
And the cost in the greedy search, denoted by h(x).
The summed cost is denoted by f(x).
35. A* Tree Search
Heuristic:
The following points should be noted w.r.t heuristics in A* search.
f(x) = g(x) + h(x)
h(x) is called the forward cost and is an estimate of the distance of the current node
from the goal node.
g(x) is called the backward cost and is the cumulative cost of a node from the root
node.
36. A* Tree Search
A* search is optimal only when for all nodes.
The forward cost for a node h(x) underestimates the actual cost h*(x)
to reach the goal.
This property of A* heuristic is called admissibility.
Admissibility =
39. A* Graph Search
A* tree search works well, except that it takes time re-exploring the
branches it has already explored.
If the same node has expanded twice in different branches of the search
tree, A* search might explore both of those branches, thus wasting time
A* Graph Search removes this limitation by adding this rule:
Do not expand the same node more than once.
40. A* Graph Search
Heuristic:
Graph search is optimal only when the forward cost between
two successive nodes A and B.
Given by h(A) – h (B), is less than or equal to the backward cost
between those two nodes g(A -> B).
This property of the graph search heuristic is called consistency.
41. A* Graph Search
Use graph searches to find paths
from S to G in the following graph.
43. Means Ends Analysis
A problem-solving technique that identifies the current state, defines the
end goal and determines the action plan to reach the end state in a
modular way.
End Goals are split into sub-goals, and sub-sub goals and then action
plans are drawn to achieve sub-goals first and then move towards
achieving the main goal progressively.
44. Means Ends Analysis
Mostly problem-solving strategies will have either forward actions or
backward actions.
Means end analysis (MEA) is an important concept in artificial
intelligence (AI) because it enhances problem resolution.
45. How does the Means-Ends Analysis
work?
First, the system evaluates the current state to establish whether there
is a problem. If a problem is identified, then it means that an action
should be taken to correct it.
The second step involves defining the target or desired goal that needs
to be achieved.
The target goal is split into sub-goals, that are further split into other
smaller goals.
46. Continue…
This step involves establishing the actions or operations that will be carried out to
achieve the end state.
In this step, all the sub-goals are linked with corresponding executable actions
(operations).
After that is done, intermediate steps are undertaken to solve the problems in the
current state. The chosen operators will be applied to reduce the differences between
the current state and the end state.
This step involves tracking all the changes made to the actual state. Changes are made
until the target state is achieved.
47.
48. Conclusion
The means-End analysis provides a logical action plan to overcome any
problems in General Management, Personal life.
In Artificial Intelligence, Mean Ends Analysis offers a methodology to
optimize the search operations to save time and effort.
