ARTIFICIAL INTELLIGENCE

1. ARTIFICIAL INTELLGENCE & ROBOTICS
AI INTRODUCTION
CSE DEPARTMENT OF MEWAR UNIVERSITY CHITTORGARGH, WELCOMES ALL OF YOU
NAME: SHIV KUMAR (CSE DEPARTMENT)

2. CONTENTS
 INTRODUCTION TO AI
 AGENT & ROBOTICS
 ENVIRONMENT
 LOGICS & REASONING- probability & mathematical logics
 AI PROBLEM
 PROBLEM SOLVING TECHNIQUES
 SEARCH TECHNIQUES
 PLANNING
 RULE BASE SYSTEM
 PRODUCTION SYSTEM
 EXPERT SYSTEM
 KR & REASONING
 GA
 NN
 REINFORCEMENT LEARNING
 NLP
 PROLOG
 LISP
 PYTHON
 REFERENCE BOOKS
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA

3. DEFINITION
What is AI ?
Artificial Intelligence is concerned with the design of intelligence in an artificial device.
The term was coined by McCarthy in 1956.
There are two ideas in the definition.
1. Intelligence
2. artificial device
What is intelligence?
– Is it that which characterize humans? Or is there an absolute standard of judgement?
– Accordingly there are two possibilities:
– A system with intelligence is expected to behave as intelligently as a human
– A system with intelligence is expected to behave in the best possible manner
– Secondly what type of behavior are we talking about?
– Are we looking at the thought process or reasoning ability of the system?
– Or are we only interested in the final manifestations of the system in terms of its actions?
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA

4. DEFINITION
What is involved in Intelligence
A) Ability to interact with the real world
- to perceive, understand, and act
- speech recognition, understanding, and synthesis
- image understanding (computer vision)
B) Reasoning and Planning
- modeling the external world
- problem solving, planning, and decision making
- ability to deal with unexpected problems, uncertainty
C) Learning and Adaptation
- we are continuously learning and adapting
- Also: we want systems that adapt to us!
- Major thrust of industry research.
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA

5. DEFINITION
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA

6. DEFINITION
COMPUTER IS THE BASIC NEED OF OUR DAILY LIFE, IT IS EVERY WHERE LIKE GOD IN THE PRESENT ERA
Rich and Knight: the study of how to make computers do things which, at the
moment, people do better.
Handbook of AI: the part of computer science concerned with designing
intelligent computer systems, that is, systems that exhibit the characteristics we
associate with intelligence in human behavior -
understanding language, learning, reasoning, solving problems, etc.
Dean, Allen and Aloimonos: the design and study of the computer
programs that behave intelligently.
Russell and Norvig: the study of [rational] agents that exist in an environment
and perceive and act. ******

7. DEFINITION

8. GOALS OF AI

9. APPROACHES OF AI
Strong AI aims to build machines that can truly reason and solve problems. These machines
should be self aware and their overall intellectual ability needs to be indistinguishable from
that of a human being. Excessive optimism in the 1950s and 1960s concerning strong AI has
given way to an appreciation of the extreme difficulty of the problem. Strong AI maintains
that suitably programmed machines are capable of cognitive mental states.
Weak AI: deals with the creation of some form of computer-based artificial intelligence that
cannot truly reason and solve problems, but can act as if it were intelligent. Weak AI holds
that suitably programmed machines can simulate human cognition.
Applied AI: aims to produce commercially viable "smart" systems such as, for example, a
security system that is able to recognise the faces of people who are permitted to enter a
particular building. Applied AI has already enjoyed considerable success.
Cognitive AI: computers are used to test theories about how the human mind works--for
example, theories about how we recognise faces and other objects, or about how we solve
abstract problems.

10. APPROACHES OF AI

11. APPROACHES OF AI

12. APPROACHES OF AI

13. APPROACHES OF AI

14. APPROACHES OF AI
Turing Test in AI
In 1950, Alan Turing introduced a test to check whether a machine can
think like a human or not, this test is known as the Turing Test. In this
test, Turing proposed that the computer can be said to be an intelligent
if it can mimic human response under specific conditions.
Turing Test was introduced by Turing in his 1950 paper, "Computing
Machinery and Intelligence," which considered the question, "Can
Machine think?"

15. APPROACHES OF AI
Turing Test in AI
Consider, Player A is a computer, Player B is human, and Player C is an
interrogator. Interrogator is aware that one of them is machine, but he
needs to identify this on the basis of questions and their responses.
The conversation between all players is via keyboard and screen so the
result would not depend on the machine's ability to convert words as
speech.
The test result does not depend on each correct answer, but only how
closely its responses like a human answer. The computer is permitted
to do everything possible to force a wrong identification by the
interrogator.
"In 1991, the New York businessman Hugh Loebner announces the
prize competition, offering a $100,000 prize for the first computer to
pass the Turing test. However, no AI program to till date, come close to
passing an undiluted Turing test".

16. APPROACHES OF AI
Turing Test in AI
Consider, Player A is a computer, Player B is human, and Player C is an
interrogator. Interrogator is aware that one of them is machine, but he
needs to identify this on the basis of questions and their responses.
The conversation between all players is via keyboard and screen so the
result would not depend on the machine's ability to convert words as
speech.
The test result does not depend on each correct answer, but only how
closely its responses like a human answer. The computer is permitted
to do everything possible to force a wrong identification by the
interrogator.
"In 1991, the New York businessman Hugh Loebner announces the
prize competition, offering a $100,000 prize for the first computer to
pass the Turing test. However, no AI program to till date, come close to
passing an undiluted Turing test".

17. APPROACHES OF AI
Chatbots to attempt the Turing test:
ELIZA: ELIZA was a Natural language processing computer program
created by Joseph Weizenbaum. It was created to demonstrate the
ability of communication between machine and humans. It was one of
the first chatterbots, which has attempted the Turing Test.
Parry: Parry was a chatterbot created by Kenneth Colby in 1972. Parry
was designed to simulate a person with Paranoid
schizophrenia(most common chronic mental disorder). Parry was
described as "ELIZA with attitude." Parry was tested using a variation
of the Turing Test in the early 1970s.
Eugene Goostman: Eugene Goostman was a chatbot developed in
Saint Petersburg in 2001. This bot has competed in the various number
of Turing Test. In June 2012, at an event, Goostman won the
competition promoted as largest-ever Turing test content, in which it
has convinced 29% of judges that it was a human.Goostman
resembled as a 13-year old virtual boy.

18. APPROACHES OF AI
The Chinese Room Argument:
There were many philosophers who really disagreed with the complete
concept of Artificial Intelligence. The most famous argument in this list
was "Chinese Room."
In the year 1980, John Searle presented "Chinese Room" thought
experiment, in his paper "Mind, Brains, and Program," which was
against the validity of Turing's Test. According to his argument,
"Programming a computer may make it to understand a
language, but it will not produce a real understanding of
language or consciousness in a computer."
He argued that Machine such as ELIZA and Parry could easily pass the
Turing test by manipulating keywords and symbol, but they had no real
understanding of language. So it cannot be described as "thinking"
capability of a machine such as a human.

19. APPROACHES OF AI
Features required for a machine to pass the Turing test:
•Natural language processing: NLP is required to communicate with
Interrogator in general human language like English.
•Knowledge representation: To store and retrieve information during
the test.
•Automated reasoning: To use the previously stored information for
answering the questions.
•Machine learning: To adapt new changes and can detect generalized
patterns.
•Vision (For total Turing test): To recognize the interrogator actions
and other objects during a test.
•Motor Control (For total Turing test): To act upon objects if
requested.

20. APPROACHES OF AI
I Building exact models of human cognition
• view from psychology and cognitive science
II The logical thought approach
• emphasis on ``correct'' inference
III Building rational ``agents''
• agent: something that perceives and acts
• emphasis on developing methods to match or exceed
human
performance [in certain domains]. Example: Deep Blue.

21. What can AI systems do?
What can AI systems do
Today’s AI systems have been able to achieve limited success in some of these tasks.
• In Computer vision, the systems are capable of face recognition
• In Robotics, we have been able to make vehicles that are mostly autonomous.
• In Natural language processing, we have systems that are capable of simple
machine translation.
• Today’s Expert systems can carry out medical diagnosis in a narrow domain
• Speech understanding systems are capable of recognizing several thousand words
continuous speech
• Planning and scheduling systems had been employed in scheduling experiments
with the Hubble Telescope.
• The Learning systems are capable of doing text categorization into about a 1000
topics
• In Games, AI systems can play at the Grand Master level in chess (world
champion), checkers, etc.

22. What can AI systems NOT do yet?
What can AI systems NOT do yet?
• Understand natural language robustly (e.g., read and understand articles in a
newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence

23. famous AI system
1. ALVINN:
Autonomous Land Vehicle In a Neural Network
In 1989, Dean Pomerleau at CMU created ALVINN. This is a system which learns
to control vehicles by watching a person drive. It contains a neural network whose
input is a 30x32 unit two dimensional camera
2. Deep Blue
In 1997, the Deep Blue chess program created by IBM, beat the current world chess
champion, Gary Kasparov.
3. Machine translation
A system capable of translations between people speaking different languages will
be a remarkable achievement of enormous economic and cultural benefit. Machine
translation is one of the important fields of endeavour in AI. While some translating
systems have been developed, there is a lot of scope for improvement in translation
quality.

24. famous AI system
4. Autonomous agents
In space exploration, robotic space probes autonomously monitor their surroundings,
make decisions and act to achieve their goals.
NASA's Mars rovers successfully completed their primary three-month missions in
April, 2004. The Spirit rover had been exploring a range of Martian hills that took
two months to reach. It is finding curiously eroded rocks that may be new pieces to
the puzzle of the region's past. Spirit's twin, Opportunity, had been examining
exposed rock layers inside a crater
5. Internet agents
The explosive growth of the internet has also led to growing interest in internet
agents to monitor users' tasks, seek needed information, and to learn which
information is most useful

25. AI TECHNIQUES

26. AI COMMON TECHNIQUES
Representation- Knowledge needs to be represented somehow – perhaps as a
series of if-then rules, as a frame based system, as a semantic network, or in the
connection weights of an artificial neural network.
Learning- Automatically building up knowledge from the environment – such as
acquiring the rules for a rule based expert system, or determining the appropriate
connection weights in an artificial neural network.
Rules- These could be explicitly built into an expert system by a knowledge
engineer, or implicit in the connection weights learnt by a neural network.
Search- This can take many forms – perhaps searching for a sequence of states that
leads quickly to a problem solution, or searching for a good set of connection
weights for a neural network by minimizing a fitness function.

27. AI COMPONENTS
AND OTHERS ARE- KB, CONTROL STRATEGIES, INFERECE MECHANISM ETC

28. AI, its parent disciplines and application areas

29. AI FOUNDATION / ROOTS
• Philosophy
• Mathematics
• Psychology/Cognitive Science
• ECONOMICS
• LINGUISTICS
• CONROL THEORY
• COMPUTER SCIENCE

30. SUB AREAS OF AI
• Neural Networks – e.g. brain modelling, time series prediction,
classification
• Evolutionary Computation – e.g. genetic algorithms, genetic
programming
• Vision – e.g. object recognition, image understanding
• Robotics – e.g. intelligent control, autonomous exploration
• Expert Systems – e.g. decision support systems, teaching systems
• Speech Processing– e.g. speech recognition and production
• Natural Language Processing – e.g. machine translation
• Planning – e.g. scheduling, game playing
• Machine Learning – e.g. decision tree learning, version space
learning

31. AI HISTORY
Prof. Peter Jackson (University of Edinburgh) classified the history of AI into three periods
as:
1. Classical Period:
It was started from 1950. In 1956, the concept of Artificial Intelligence came into
existance. During this period, the main research work carried out includes game plying,
theorem proving and concept of state space approach for solving a problem.
2. Romantic Period:
It was started from the mid 1960 and continues until the mid 1970. During this period
people were interested in making machine understand, that is usually mean the
understanding of natural language. During this period the knowledge representation
technique “semantic net” was developed.
3. Modern Period:
It was started from 1970 and continues to the present day. This period was developed to
solve more complex problems. This period includes the research on both theories and
practical aspects of Artificial Intelligence. This period includes the birth of concepts
like Expert system, Artificial Neurons, Pattern Recognition etc. The research of
the various advanced concepts of Pattern Recognition and Neural Network are
still going on.

32. AI ARCHITECTURE
for GENERAL INTELLIGENCE
The AGIRI website lists several features, describing machines
with human-level, and even superhuman, intelligence.
that generalize their knowledge across different domains.
that reflect on themselves.
and that create fundamental innovations and insights.
Figure . Attention and Action Selection

33. AI ARCHITECTURE-GENERAL INTELLIGENCE
Figure . Learning

34. AI ARCHITECTURE- LIDA
Learning Intelligent Distribution Agent
• IDA denotes a conceptual and computational model of human cognition.
IDA, an acronym for Intelligent Distribution1 Agent, is an autonomous software
agent, which automates the tasks of the detailers[The US Navy has about
350,000 sailors. As each sailor comes to the end of a certain tour of duty, he or
she needs a new billet, a new job. The Navy employs some 300 detailers, as they
call them, personnel officers who assign these new billets. A detailer dialogs with
sailors, usually over the telephone, but sometime by email.]
Figure . Working Memory

35. AI ARCHITECTURE- SLOMAN’S

36. REFERENCE BOOKS
• INTRODUCTION OF COMPUTER SCIENCE ‘C’ EDITION- ULMAN
• ‘C’ PROGRAMMING LANGUAGE- BRAIAN & RITCHIE
• PROGRAMMING IN ‘C’ –ASHOK N. KAMTHANE
• LET US ‘C’ – KANETKAR
• FUNDAMENTAL OF COMPUTER= RAJA RAMAN
International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering