This document provides an overview of the first session of an Artificial Intelligence and Machine Learning course. It introduces key concepts in AI like intelligent agents, problem solving by search, and uninformed and informed search strategies. It defines AI as the ability of computers to learn and think. The session covered problem solving approaches, agent types, and rational agents. The topics to be covered in the next session include the different types of agents.

1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and engineering
(AI/ML)
Session 1
by
Asst.Prof.M.Gokilavani
VITS
2/23/2023 Department of CSE (AI/ML) 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.
2/23/2023 Department of CSE (AI/ML) 2

3. Unit I
• Problem solving by search-I: Introduction to AI, Intelligent Agents.
• Problem solving by search-II: Problem solving agents, searching for
solutions
• Uniformed search strategies: BFS, Uniform cost search, DFS, Iterative
deepening Depth-first search, Bidirectional search,
• Informed ( Heuristic) search strategies: Greedy best-first search, A*
search, Heuristic functions
• Beyond classical search: Hill- climbing Search, Simulated annealing
search, Local search in continuous spaces, Searching with non-
deterministic Actions, searching with partial observations, online
search agents and unknown environments.
2/23/2023 Department of CSE (AI/ML) 3

4. Topics covered in session 1
2/23/2023 Department of CSE (AI/ML) 4
• Problem solving by search-I: Introduction to AI, Intelligent Agents.
• Problem solving by search-II: Problem solving agents, searching for
solutions
• Uniformed search strategies: BFS, Uniform cost search, DFS, Iterative
deepening Depth-first search, Bidirectional search,
• Informed ( Heuristic) search strategies: Greedy best-first search, A*
search, Heuristic functions
• Beyond classical search: Hill- climbing Search, Simulated annealing
search, Local search in continuous spaces, Searching with non-
deterministic Actions, searching with partial observations, online
search agents and unknown environments.

5. What is Artificial Intelligence ?
• Making computers that think?
• The automation of activities we associate with human thinking, like
decision making, learning ... ?
• The art of creating machines that perform functions that require
intelligence when performed by people ?
• The study of mental faculties through the use of computational models
?
2/23/2023 Department of CSE (AI/ML) 5

6. What is Artificial Intelligence ?
• Artificial Intelligence is the ability of a computer program to learn and
think.
• John McCarthy coined the term ‘Artificial Intelligence’ in the 1950s.
• He said, ‘Every aspect of learning or any other feature of intelligence
can in principle be so precisely described that a machine can be made
to simulate it.
• An attempt will be made to find how to make machines use language,
form abstractions, and concepts, solve kinds of problems now reserved
for humans, and improve themselves.’
2/23/2023 Department of CSE (AI/ML) 6

7. What is Artificial Intelligence ?
2/23/2023 Department of CSE (AI/ML) 7
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

8. Systems that think like humans: cognitive
modeling
• Humans as observed from ‘inside’
• How do we know how humans think?
• Introspection vs. psychological experiments
• Cognitive Science
• “The exciting new effort to make computers think … machines with
minds in the full and literal sense” (Haugeland)
• “[The automation of] activities that we associate with human thinking,
activities such as decision-making, problem solving, learning …”
(Bellman).
2/23/2023 Department of CSE (AI/ML) 8

9. Systems that think ‘rationally’ "laws of
thought"
• Humans are not always ‘rational’
• Rational - defined in terms of logic?
• Logic can’t express everything (e.g. uncertainty)
• Logical approach is often not feasible in terms of computation time
(needs ‘guidance’)
• “The study of mental facilities through the use of computational
models” (Charniak and McDermott)
• “The study of the computations that make it possible to perceive,
reason, and act” (Winston)
2/23/2023 Department of CSE (AI/ML) 9

10. Systems that act like humans
• The Turing Test approach
• a human questioner cannot tell if
• there is a computer or a human answering his question, via teletype (remote
communication)
• The computer must behave intelligently
• Intelligent behavior
• to achieve human-level performance in all cognitive tasks
2/23/2023 Department of CSE (AI/ML) 10

11. Turning test
Example: Program ELIZA simulating a psychiatrist.
Person: I miss my children
ELIZA: “Why do you miss your children?” or “ Tell me more
about your family”
ELIZA is programmed to ask pre-determined questions and parrot
segments of your responses back to you. Hence Turing test may not be
such a good judge of machine intelligence after all.
2/23/2023 Department of CSE (AI/ML) 11

12. What is meant by Turning test?
• Turing test was proposed in 1950.
• It is a test to decide whether or not a particular machine is intelligent.
• Predicted that by 2000, a machine might have a 30% chance of fooling a lay
person for 5 minutes.
2/23/2023 Department of CSE (AI/ML) 12

13. Systems that act like humans
• These cognitive tasks include:
• Natural language processing
• for communication with human
• Knowledge representation
• to store information effectively & efficiently
• Automated reasoning
• to retrieve & answer questions using the stored information
• Machine learning
• to adapt to new circumstances
2/23/2023 Department of CSE (AI/ML) 13

14. Systems that act rationally:“Rational agent”
• Rational behavior: doing the right thing
• The right thing: that which is expected to maximize goal achievement,
given the available information
• Giving answers to questions is ‘acting’.
• I don't care whether a system:
• replicates human thought processes
• makes the same decisions as humans
• uses purely logical reasoning
• Logic  only part of a rational agent, not all of rationality
• Sometimes logic cannot reason a correct conclusion
• At that time, some specific (in domain) human knowledge or information is
used
2/23/2023 Department of CSE (AI/ML) 14

15. 2/23/2023 Department of CSE (AI/ML) 15

16. What is meant by agents?
• In general, an entity that interacts with its environment.
• perception through sensors
• Actions through effectors or actuators
• Examples:
• Human agent
• eyes, ears, skin, taste buds, etc. for sensors
• hands, fingers, legs, mouth, etc. for actuators
• powered by muscles
• Robot
• camera, infrared, bumper, etc. for sensors
• grippers, wheels, lights, speakers, etc. for actuators
2/23/2023 Department of CSE (AI/ML) 16

17. Agents and Environment
• An agent perceives its environment through sensors
• The complete set of inputs at a given time is called a
percept
• The current percept, or a sequence of percepts may
influence the actions of an agent
• It can change the environment through actuators
• An operation involving an actuator is called an
action
• Actions can be grouped into action sequences
2/23/2023 Department of CSE (AI/ML) 17

18. Agents and Their Actions
• A rational agent does the right thing
• The action that leads to the best outcome under the given
circumstances
• An agent function maps percept sequences to actions
• Abstract mathematical description
• An agent program is a concrete implementation of the respective
function
• It runs on a specific agent architecture (platform)
• Problems
• What is the right thing?
• How do you measure the best outcome?
2/23/2023 Department of CSE (AI/ML) 18

19. Performance of Agents
• Criteria for measuring the outcome and the expenses of the agent
• Often subjective, but should be objective
• Task dependent
• Time may be important
2/23/2023 Department of CSE (AI/ML) 19

20. Performance evaluation examples
• Vacuum agent
• Number of tiles cleaned during a certain period
• Based on the agents report, or validated by an objective authority
• Doesn't consider expenses of the agent, side effects
• Energy, noise, loss of useful objects, damaged furniture, scratched
floor
• Might lead to unwanted activities
• Agent re-cleans clean tiles, covers only part of the room, drops dirt on
tiles to have more tiles to clean, etc.
2/23/2023 Department of CSE (AI/ML) 20

21. Rational Agents
 An agent is an entity that perceives and acts
 This course is about designing rational agents
 Abstractly, an agent is a function from percept histories to actions:
[f: P*  A]
 For any given class of environments and tasks, we seek the agent (or class
of agents) with the best performance
 Caveatcomputational limitations make perfect rationality unachievable
 design best program for given machine resources
2/23/2023 Department of CSE (AI/ML) 21

22. Purpose of Rational Agents
• Study AI as rational agent –
2 advantages:
• It is more general than using logic only
• Because: LOGIC + Domain knowledge
• It allows extension of the approach with more scientific methodologies
2/23/2023 Department of CSE (AI/ML) 22

23. Topics to be covered in next session 2
• Types of Agent
2/23/2023 Department of CSE (AI/ML) 23
Thank you!!!