AI

1. Artificial Intelligence
“the science and engineering of making intelligent machines”

2. CO
Number
Course Outcome Statement
Weightage***
in %
CO1
Summarize different types of AI environments, transform a
given real world problem to state space problem.
10
CO2
Apply the relevant uniform search algorithms and heuristics
search strategies based on the given state space.
25
CO3
Implement the local search strategies to solve the given
Constraint Satisfaction Problem.
10
CO4
Apply the suitable Adversarial search techniques for the
given multi-agent environment.
15
CO5
Utilize propositional logics and probabilistic reasoning to
apply knowledge representation for the given certain and
uncertain problem respectively.
15
CO6
Construct plan graph using planning techniques for the
given state space.
15
CO7
Explain the stages and issues in the development of an
expert system.
10

3. Syllabus
Introduction, Overview of Artificial intelligence: Problems of AI, AI technique, Tic - Tac - Toe problem.
Intelligent Agents, Agents & environment, nature of environment, structure of agents, goal based
agents, utility based agents, learning agents.
Problem Solving: Defining the problem as state space search, production system, problem
characteristics, issues in the design of search programs.
Search techniques: Problem solving agents, searching for solutions; uniform search strategies:
breadth first search, depth first search, depth limited search, bidirectional search, comparing uniform
search strategies. Heuristic search strategies Greedy best-first search, A* search, AO* search, memory
bounded heuristic search: local search algorithms & optimization problems: Hill climbing search,
simulated annealing search, local beam search.
Constraint satisfaction problems: Local search for constraint satisfaction problems. Adversarial
search, Games, optimal decisions & strategies in games, the minimax search procedure, alpha-beta
pruning, additional refinements, iterative deepening.
Knowledge & reasoning: Knowledge representation issues, representation & mapping, approaches to
knowledge representation. Predicate logic, representing simple fact in logic, representing instant & ISA
relationship, computable functions & predicates, resolution, natural deduction. Representing
knowledge using rules, Procedural verses declarative knowledge, logic programming, forward verses
backward reasoning, matching, control knowledge.
Probabilistic reasoning: Representing knowledge in an uncertain domain, the semantics of Bayesian
networks, Dempster-Shafer theory, Planning Overview, components of a planning system, Goal stack
planning, Hierarchical planning, other planning techniques.
Expert Systems: Representing and using domain knowledge, expert system shells, and knowledge
acquisition.

4. TEXT BOOK:
1. Stuart J. Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” , 4th
edition, Pearson, 2020.
2. Elaine Rich, Kevin Knight and Shivashankar B Nair, “Artificial Intelligence”, Third
Edition, McGraw Hill Education India, 2010.
NPTEL:
https://onlinecourses.nptel.ac.in/noc23_cs05/preview - An Introduction to Artificial
Intelligence - By Prof. Mausam | IIT Delhi

5. CO
Number
Course Outcome Statement
Weightage***
in %
CO1
Develop solutions using relevant uninformed search
strategies to solve the given state space problem. 15
CO2
Construct a solution using suitable heuristic searching
algorithms for the given problem statement 15
CO3
Implement a suitable optimization algorithm for the real-
world problem. 15
CO4
Apply an adversarial search strategy for the given
gaming environment and the CSP problem. 25
CO5
Construct a planning graph to solve the considered real
world problem. 15
CO6
Apply various probabilistic decision-making algorithms
on considering a real time dataset for evaluation. 15
20CB680 - Artificial Intelligence Lab

6. S.No Name of the Experiment
No. of
sessions
Course
Outcome
1. Implement a solution for the Tic-Tac-Toe with O and X & Water Jug Problem 1 CO1
2.
Implement an uninform searching strategy to detect a cycle and the strongly
connected components in a directed graph using BFS and DFS respectively.
1 CO1
3.
Implement a program to find the shortest path between the source and the
destination node using A* and AO* heuristic searching algorithms.
1 CO2
4. Implement a Greedy Heuristic solution for the travelling salesman problem. 1 CO2
5.
Implement an Optimization Algorithm, Hill Climbing algorithm for the N-
Queen Problem.
1 CO3
6.
Implement a program to solve the crypt arithmetic puzzle, a CSP problem
using Backtracking.
2 CO4
7.
Implement the adversarial search MinMax algorithm with and without alpha
beta pruning for a two-player gaming environment.
1 CO4
8.
Implement a program to solve the Block World Problem using goal stack
planning.
1 CO5
9. Implement Bayesian Belief Network 1 C06
10. Implement a k-means clustering algorithm 1 C06
11. Implement Decision Tree for any considered application of decision making. 1 C06
Total 12

7. AI and its dependencies

8. MOTIVATION

9. Foundation of AI
• Philosophy (428BC-present)
• Can formal rules be used to draw valid conclusions?
• How does the mental mind arise from a physical
• brain?
• Where does knowledge come from?
• How does knowledge lead to action?
• Mathematics (800 – present)
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?

10. Foundation of AI
• Economics (1776-present)
• How should we make decisions so as to maximize
• payoff?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in
• the future?
• Neuroscience (1861-present)
• How do brains process information?
• Psychology (1879-present)
• How do humans and animals think and act?
• Linguistics (1957-present)
• How does language relate to thought?

11. HISTORY

12. 1946: ENIAC heralds the dawn of Computing

13. I propose to consider the question:
“Can machines think?”
--Alan Turing, 1950
1950: Turing asks the question….

14. 1956: A new field is born
• Proposed that a 2 month, 10 man
study of artificial intelligence be
carried out during the summer of
1956 at Dartmouth College in
Hanover, New Hampshire.
• - Dartmouth AI Project Proposal;
J. McCarthy et al.; Aug. 31, 1955.
• John McCarthy (worked in chess
– LISP), Allen Newell & Herbert
Simon from Carnegie Tech
(Theory for Theorems) and
Marvin Minsky (MIT)

19. AI SUCCESS

20. 1996: EQP proves that
Robbin’sAlgebras are all boolean
[An Argonne lab program] has come up with a major mathematical
proof that would have been called creative if a human had thought of it.
-New York Times, December, 1996
----- EQP 0.9, June 1996 -----
The job began on eyas09.mcs.anl.gov, Wed Oct 2 12:25:37 1996
UNIT CONFLICT from 17666 and 2 at 678232.20 seconds.
PROOF
2 (wt=7) [] -(n(x + y) = n(x)).
3 (wt=13) [] n(n(n(x) + y) + n(x + y)) = y.
5 (wt=18) [para(3,3)] n(n(n(x + y) + n(x) + y) + y) = n(x + y).
6 (wt=19) [para(3,3)] n(n(n(n(x) + y) + x + y) + y) = n(n(x) + y).
…….
17666 (wt=33) [para(24,16426),demod([17547])] n(n(n(x) + x) ….

21. 1997: HAL 9000 becomes operational
in fictional Urbana, Illinois
…by now, every intelligent person knew that
H-A-L is derived from Heuristic ALgorithmic
-Dr. Chandra, 2010: Odyssey Two
HAL 9000 is a fictional artificial intelligence character
HAL has been shown to be capable of speech, speech recognition, facial recognition, natural
language processing, lip reading, art appreciation, interpreting emotional behaviours,
automated reasoning, spacecraft piloting and playing chess

22. 1997: Deep Blue ends Human
Supremacy in Chess
I could feel human-level intelligence across the room
-Gary Kasparov, World Chess Champion (human)
In a few years, even a single victory
in a long series of games would be the triumph of human genius.
vs.

23. For two days in May, 1999, an AI Program called Remote Agent
autonomously ran Deep Space 1 (some 60,000,000 miles from earth)
Real-time Execution
Adaptive Control
Hardware
Scripted
Executive Generative
Planner &
Scheduler
Generative
Mode Identification
& Recovery
Scripts
Mission-level
actions &
resources
component models
ESL
Monitors
Goals
1999: Remote Agent takes
Deep Space 1 on a galactic ride

24. 2004 & 2009

25. 2005: Cars Drive Themselves
• Stanley and three
other cars drive
themselves over a
132 mile
mountain road
• H1ghlander and
Sandstorm
https://www.youtube.com/watch?v=7a6GrKqOxeU

26. 2007: Robots Drive on Urban Roads
11 cars drove themselves on
urban streets (for DARPA
Urban Challenge)
https://www.youtube.com/watch?v=aHYRtOvSx-M

27. Recentmost Success 2011
IBM’s WATSON
And Ken Jennings pledges respect to the new Computer Overlords..

28. PRESENT
STATE-OF-ART

31. Europa Mission ~ 2018

32. • Provide a standard problem
where a wide range of
technologies can be
integrated and examined
• By 2050, develop a team of
fully autonomous humanoid
robots that can win against
the human world champion
team in soccer.

37. AI Today
• Autonomous planning & Control
• Scheduling
• Game playing
• Diagnosis
• Logistics Planning
• Robotics
• Language Understanding and Problem Solving