Replicate human intelligence Solve Knowledge-intensive tasks An intelligent connection of perception and action Building a machine which can perform tasks that requires human intelligence such as: Proving a theorem Playing chess Plan some surgical operation Driving a car in traffic Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user. What Comprises to Artificial Intelligence? Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: Mathematics Biology Psychology Sociology Computer Science Neurons Study Statistics Advantages of Artificial Intelligence Following are some main advantages of Artificial Intelligence: High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information. High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game. High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy. Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky. Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement. Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.

1. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Solving Problems by Searching
• Reflex agent is simple(Cannot Operate in Well Environment, its Action
–Based on Current Situation and ignoring the History of Perception)
e.g Car Front Brakes
• base their actions on
• a direct mapping from states to actions
• but cannot work well in environments
• which this mapping would be too large to store
• and would take too long to learn
• Hence, goal-based agent is used

2. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Problem-solving agent
• Problem-solving agent
• A kind of goal-based agent – Choose their actions in order to achieve Goals.
• This Allows the Agent to a way of Choosing among Multiple Possibilities,
selecting the one which reaches a Goal states.
• It Requires Searching and Planning Techniques. E.g. GPS – Finding a path to
certain Destination.
• It solves problem by
• finding sequences of actions that lead to desirable states (goals)
• To solve a problem,
• the first step is the goal formulation, based on the current situation

3. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Problem Solving Agents :
• Intelligent Agent are Supposed to “ To Maximize their
Performance Measure ”
• Achievement : It can Adopt a Goal and Aim at Satisfying.

4. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

5. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

6. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

7. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

8. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

9. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Goal formulation
• The goal is formulated
• as a set of world states, in which the goal is satisfied
• Reaching from initial state 🡪 goal state
• Actions are required
• Actions are the operators
• causing transitions between world states
• Actions should be abstract enough at a certain degree, instead of
very detailed
• E.g., turn left VS turn left 30 degree, etc.

10. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Problem formulation
Well-defined problems and solutions
A problem is defined by 5 components:
• Initial state
• Actions
• Transition model or
(Successor functions)
• Goal Test.
• Path Cost.

11. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY

12. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Well-defined problems and solutions
• A problem is defined by 4 components:
• The initial state
• that the agent starts in
• The set of possible actions
• Transition model: description of what each action does.
(successor functions): refer to any state reachable from given
state by a single action
• Initial state, actions and Transition model define the state
space
• the set of all states reachable from the initial state by any
sequence of actions.
• A path in the state space:
• any sequence of states connected by a sequence of actions.

13. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Well-defined problems and solutions
• The goal test
• Applied to the current state to test
• if the agent is in its goal
-Sometimes there is an explicit set of possible goal states.
(example: in Amman).
-Sometimes the goal is described by the properties
• instead of stating explicitly the set of states
• Example: Chess
• the agent wins if it can capture the KING of the opponent on next
move ( checkmate).
• no matter what the opponent does

14. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Well-defined problems and solutions
• A path cost function,
• assigns a numeric cost to each path
• = performance measure
• denoted by g
• to distinguish the best path from others
• Usually the path cost is
• the sum of the step costs of the individual actions (in the action list)

15. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Well-defined problems and solutions
• Together a problem is defined by
• Initial state
• Actions
• Successor function
• Goal test
• Path cost function
• The solution of a problem is then
• a path from the initial state to a state satisfying the goal test
• Optimal solution
• the solution with lowest path cost among all solutions

16. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Formulating problems
• Besides the five components for problem formulation
• anything else?
• Abstraction
• the process to take out the irrelevant information
• leave the most essential parts to the description of the states
( Remove detail from representation)
• Conclusion: Only the most important parts that are
contributing to searching are used

17. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Problem-Solving Agents
• agents whose task is to solve a particular problem (steps)
• goal formulation
• what is the goal state
• what are important characteristics of the goal state
• how does the agent know that it has reached the goal
• are there several possible goal states
• are they equal or are some more preferable
• problem formulation
• what are the possible states of the world relevant for solving the
problem
• what information is accessible to the agent
• how can the agent progress from state to state

18. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Example: Romania
• On holiday in Romania; currently in Arad.
• Flight leaves tomorrow from Bucharest
• Formulate goal:
• be in Bucharest
• Formulate problem:
• Initial states:
• actions:
• Transition Model
• Goal Test
• Path Cost
• Search solution :
sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest
• Execution

19. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Example: Romania

20. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Single-state problem formulation
• Initial State : In(Arad)
• Action : In(Arad){Go(Sibiu),Go(Timisoara),Go(Zerind)}
• Transition Model : Result(In(Arad),Go(Zerind))=In(Zerind)
• Goal Test : In(Zerind) ==In (Bucharest) Reached the Destination??
• Path Cost : Kms , Time in Hrs
• A solution is a sequence of actions leading from the initial state to a
goal state

21. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Example problems
• Toy problems
• those intended to illustrate or exercise various problem-solving methods
• E.g., puzzle, chess, etc.
• Real-world problems
• tend to be more difficult and whose solutions people actually care about
• E.g., Design, planning, etc.

22. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 2 : Toy problems
Example : Vacuum world

23. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
Initial State : Any State can be Designed as Initial State
Action : L,R,S (If Large Environment UP & DOWN)
Transition Model : L->R – Successor – Suck the Dirt and Clean
R->L- Suck the Dirt and Clean
Goal Test : Check whether All the all Squares are Clean
Path Cost : Each Step cost 1(no of steps in a path)

24. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 3 :The 8-puzzle

25. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 3 :The 8-puzzle
Initial State : Any State can be Designed as Initial State
Action : L,R,S ,UP ,DOWN)
Transition Model : U->D,D->U,L->R,R->L Successor –
Action and State Returning State
Goal Test : Check whether it reaches the Goal State
Path Cost : Each Step cost 1(no of steps in a path)

26. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 3 :The 8-puzzle

27. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 4 : The 8-queens
Q
Q
Q
Q
Q
Q
Q
Q

28. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
PROBLEM 4 : The 8-queens

29. SRI SHAKTHI
INSTITUTE OF ENGINEERING & TECHNOLOGY
REAL WORLD PROBLEMS
• ROUTE FINDING PROBLEMS
• TOURING PROBLEMS
• TRAVEING SALES PERSON PROBLEM
• VLSI LAYOUT
• ROBOT NAVIGATION
• AUTOMATION ASSEMBLY SEQUENCING