Intro to artificial intelligence notes

1. Artificial Intelligence
Dr. Amit Kumar, Dept of CSE, JUET, Guna

2. What is Intelligence?
Intelligence:
“the capacity to learn and solve problems” (Webster's
dictionary)
in particular,
the ability to solve novel problems
the ability to act rationally
the ability to act like humans
• Intelligence is the quality possessed by humans.
•Is the ability to think and understand instead of doing things
by instinct or automatically.
•Intelligence is their ability to understand and learn things.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

3. Intelligent behaviour
• Perceiving one’s environment,
• Acting in complex environments,
• Learning and understanding from experience,
• Reasoning to solve problems and discover hidden
knowledge,
• Knowledge applying successfully in new
situations,
• Thinking abstractly, using analogies,
• Communicating with others, and more like.
• Creativity, Ingenuity, Expressive-ness, Curiosity.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

4. What is Artificial Intelligence
•Automation of Intelligent Behavior.
•Build and understand intelligent entities or agents
•AI is a part of computer science &,as such, must be based on sound
theoretical and applied principles of that field.
•These principles include the data structures used in knowledge
representation the algorithm needed to apply that knowledge ,and the
language and programming techniques used in their implementation.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

5. Artificial Intelligence ……….
Systems that think like humans Systems that think rationally
“The exciting new effort to make
computers think… machines with minds
in the full and literal
sense.(Haugeland,1985)
“The automation of activities that we
associate with human thinking, activities
such as decision –making , problem
solving,learning…..”(Bellman,1978).
“The study of mental facility through the
use of computational models.”
(Charniak and McDermott,1985)
“The study of the computations that make
it possible to perceive, reason,and
act”.(Winston,1992)
Systems that act like humans Systems that act rationally
“The art of creating machines that perform
functions that require intelligence when
performed by people.”(Kurzweil,1990)
“The study of how to make computers do
things at which ,at the moment ,people are
better.”(Rich and Knight,1991)
“Computational intelligence is the study of
the design of intelligent agents.”(poole et.
al.,1998)
“AI is concerned with intelligent behavior
in artifacts.”(Nilsson,1998)
Dr. Amit Kumar, Dept of CSE, JUET, Guna

6. • A system is rational if it does the right thing.
• A human-centered approach must be an
empirical science, involving hypothesis and
experimental confirmation .
• A rationalist approach involves a combination
of mathematics and engineering.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

7. Understanding AI
• How knowledge is acquired, represented, and stored;
• How intelligent behaviour is generated and learned;
• How motives, emotions, and priorities are developed
and used;
• How sensory signals are transformed into symbols;
• How symbols are manipulated to perform logic, to
reason about past,and plan for future;
• How mechanisms of intelligence produce the
phenomena of illusion,belief, hope, fear, dreams,
kindness and love.”
Dr. Amit Kumar, Dept of CSE, JUET, Guna

8. “HARD” OR “STRONG” AI
• Generally, artificial intelligence research aims to create
AI that can replicate human intelligence completely.
• “Strong AI” refers to a machine that approaches or
supersedes human intelligence,
◊ if it can do typically human tasks,
◊ if it can apply a wide range of background
knowledge and
◊ if it has some degree of self-consciousness.
• “Strong AI” aims to build machines whose overall
intellectual ability is indistinguishable from that of a
human being.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

9. “SOFT” OR “WEAK” AI
• “Weak AI” refers to the use of software to study
or accomplish specific
• problem solving or reasoning tasks that do not
encompass the full range of human cognitive
abilities.
• Example : a chess program such as “Deep Blue”.
• “Weak AI” does not achieve self-awareness; it
demonstrates wide range of human-level
cognitive abilities; it is merely an intelligent, a
specific problem-solver.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

10. COGNITIVE SCIENCE
• Aims to develop, explore and evaluate theories
of how the mind works through the use of
computational models.
• The important is not what is done but how it is
done; i.e. intelligent behaviour is not enough, the
program must operate in an intelligent manner.
• Example : The Chess programs are successful,
but say little about the ways humans play chess.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

11. Goal of AI
• The definitions of AI gives four possible goals to
pursue :
1. Systems that think like humans. 2. Systems that think rationally.
3. Systems that act like humans 4. Systems that act rationally
• Traditionally, all four goals have been followed and the
approaches were:
• Most of AI works falls into category(2) and (4).
HUMAN LIKE RATIONALLY
Think (1) Cognitive Science Approach (2) Laws of thought Approach
Act (3) Turing test Approach (4) Rational Agent Approach
Dr. Amit Kumar, Dept of CSE, JUET, Guna

12. AI Approaches
The approaches followed are defined by choosing goals of the
computational model, and basis for evaluating performance of
the system.
1. Cognitive science : Think human-like
– An exciting new effort to make computers think; machines with minds, in
the full and literal sense.
– Focus is not just on behaviour and I/O, but looks at reasoning process.
– Computational model as to "how" results were obtained.
– Goal is not just to produce humanlike behaviour but to produce a
sequence of steps of the reasoning process, similar to the steps followed
by a human in solving the same task.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

13. 2.Laws of thought : Think Rationally
• The study of mental faculties through the use of computational models;ie
the study of the computations that make it possible to perceive , reason, and
act.
• Focus is on inference mechanisms that are provably correct and guarantee
an optimal solution.
• Develop systems of representation to allow inferences to be performed like
“Socrates is a man. All men are mortal. Therefore Socrates is mortal.”
• Goal is to formalize the reasoning process as a system of logical rules and
procedures for inference. The issue is, not all problems can be solved just
by reasoning and inferences.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

14. 3.Turing Test : Act human-like
• The art of creating machines that perform functions requiring
intelligence when performed by people; ie the study of how to
make computers do things which at the moment people do better.
• Focus is on action, and not intelligent behaviour centered around
representation of the world.
• A Behaviourist approach, is not concerned with how to get results
but to the similarity to what human results are.
• Example : Turing Test
– 3 rooms contain: a person, a computer, and an interrogator.
– The interrogator can communicate with the other 2 by teletype (to
avoid the machine imitate the appearance or voice of the person).
– The interrogator tries to determine which is the person and which is
the machine.
– The machine tries to fool the interrogator to believe that it is the
human, and the person also tries to convince the interrogator that it is
the human.
• If the machine succeeds in fooling the interrogator, then conclude
that the machine is intelligent.
• Goal is to develop systems that are act human-like.Dr. Amit Kumar, Dept of CSE, JUET, Guna

15. 4. Rational agent : Act Rationally
• Tries to explain and emulate intelligent behaviour in terms of
computational processes; it is concerned with the automation of
intelligence.
• Focus is on systems that act sufficiently if not optimally in all situations;
• It is passable to have imperfect reasoning if the job gets done.
• Goal is to develop systems that are rational and sufficient.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

16. AI Techniques
• Various techniques that have evolved, can be applied to a variety of
AI tasks.
• The techniques are concerned with how we represent, manipulate
and reason with knowledge in order to solve problems.
• Example :
◊ Techniques, not all "intelligent" but used to behave are
(a) Describe and match (b) Goal reduction
(c) Constraint satisfaction (d) Tree Searching
(e) Generate and test (f) Rule based systems
◊ Biology-inspired AI techniques are currently popular
(a) Neural Networks (b) Genetic Algorithms
(c) Reinforcement learning
Dr. Amit Kumar, Dept of CSE, JUET, Guna

17. Branches of AI
Logical AI
Logic is a language for reasoning; a collection of rules used while doing
logical reasoning.
Search AI
Search is a problem-solving technique that systematically consider all possible
action to find a path from initial state to target state.
Pattern Recognition (PR)
Definitions from the literature
◊ “The assignment of a physical object or event to one of pre-specified
categories” – Duda and Hart
◊ “The science that concerns the description or classification (recognition) of
measurements” – Schalkoff
◊ “The process of giving names Ω to observations X ” – Schürmann
◊ Pattern Recognition is concerned with answering the question “What is
this?” – Morse
◊ “A problem of estimating density functions in a high-dimensional space and
dividing the space into the regions of categories or classes” – FukunagaDr. Amit Kumar, Dept of CSE, JUET, Guna

18. • Knowledge Representation
Knowledge is a collection of “facts”. To manipulate these facts by a program, a suitable
representation is required. A Good representation facilitates problem solving.
• Inference
Inference is the act or process of deriving a conclusion based solely on what one already
knows; deduction of new facts from old ones. Logic capture inference.
• Common sense knowledge and reasoning
– Common sense is the mental skills that most people have.
– Common Sense is ability to analyze a situation based on its context.
◊ People can think because the brain contain vast libraries of common sense
knowledge and has means for organizing, acquiring, and using such
knowledge.
◊ Computer can not think; computers programs do many things, can play
chess at the level of best players but cannot match capabilities of a 3 year old
child at recognizing objects.
• Currently, computers lack common sense.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

19. Learning
Definitions of some learning problem :
◊ Herbert Simon 1983 – “Learning denotes changes in the system that are adaptive
in the sense that they enable the system to do the same task or tasks more
efficiently and more effectively the next time.”
◊ Marvin Minsky 1986 – “Learning is making useful changes in the working of our
mind.”
◊ Ryszard Michalski 1986 – "Learning is constructing or modifying representations
of what is being experienced."
◊ Mitchell 1997 – “A computer program is said to learn from experience E with
respect to some class of tasks T and performance measure P, if its performance
at tasks in T, as measured by P, improves with experience E.”
Planning
A plan is a representation of a course of action.Planning is a problem solving
technique. Planning is a reasonable series of actions to accomplish a goal.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

20. Epistemology
“Epistemology is the theory of knowledge;”
There are various kinds of knowledge:
◊ knowing how to do something (e.g., how to ride a bicycle),
◊ knowing someone in person, and
◊ knowing a place or a city.
Epistemology is the study of knowledge and justified belief.
Ontology
• Ontology is concerned with existence; a study of the categories of things that exist or may
exist in some domain.
• In computer and information science, an ontology is a data model that represents a
domain and is used to reason about the objects in that domain and the relations between
them.
• Ontology is used in artificial intelligence, as a form of knowledge representation about the
world or some part of it.
▪ Ontology generally describe:
◊ Individuals (instances): the basic or ground level objects
◊ Classes: sets, collections, or types of objects.
◊ Attributes: properties, features, characteristics, or parameters that objects can have and
share.
◊ Relations: ways that objects can be related to one another
▪ An ontology is a specification of a conceptualization.
Dr. Amit Kumar, Dept of CSE, JUET, Guna

21. Application of AI
• Game Playing
• Speech Recognition
• Understanding Natural Language
• Computer Vision
• Expert Systems
Dr. Amit Kumar, Dept of CSE, JUET, Guna