Artificial intelligence and Machine Learning
Subject Code: 21CS752
Chapter: 1.1,1.2.1.3,3.1,3.2,3.3,3.4.1,3.4.3
Textbook: Stuart Russell and Peter Norvig. Artificial
Intelligence: A Modern Approach, 3rd Edition. Prentice
Hall.
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SYLLABU
S
Introduction- What is Artificial Intelligence Foundation of AI
History of AI
Applications of AI
Intelligent Agents- Agents and Environment
Good Behaviour
The concept of rationality, nature of environment Structure of Agents
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ARTIFICIAL INTELLIGENCE
Artificial intelligence allows machines to replicate the capabilities of the human mind. From the
development of self-driving cars to the development of smart assistants like Siri and Alexa, AI is
a growing part of everyday life.
Artificial intelligence is a wide-ranging branch of computer science concerned with
building
smart machines capable of performing tasks that typically require human intelligence.
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What is AI
The definitions on the left measure success in terms of fidelity
to human performance,the ones on the right measure against
an ideal performance measure, called rationality.
A system is rational if it does the “right thing,”
given what it knows.
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Turing Test
(Human) judge communicates with a human and a machine over text-only channel,
Both human and machine try to act like a human, Judge tries to tell which is
which.
Numerous variants Loebner prize
Current programs nowhere close to
passing this
◦ http://www.jabberwacky.com/
◦ http://turingtrade.org/
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What is the Turing Test in Artificial
Intelligence?
NLP to communicate successfully.
Knowledge Representation to act as its memory.
Automated Reasoning to use the stored information to answer
questions and draw new conclusions.
Machine Learning to detect patterns and adapt to new
circumstances.
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Turing Test on unsuspecting judges
It is possible to (temporarily) fool humans who do not realize they may be talking to a bot
ELIZA program rephrases partner’s statements and questions (~psychotherapist)
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Modern variant: AOLiza
[fragment from
http://archives.cnn.com/2000/TECH/computing/08/29/aoliza.idg/
]
five: just asking if richard can tell me about some good lobster diving spots thats it if this is a
problem then forget iot i dont know whats going on here
AOLiza: that is interesting. please continue.
five: continue what just ask richard when he gets home about some lobster diving up hat way
thats all if you got a problem with it then forget it,this isnt worht it i dont know whats been
going on or what bev has told you but i havent done anything to anybody iam the one that got
used and dumped
AOLiza: tell me more about that.
five: about what
AOLiza: what do you think?
five: therers nothing to tell she us3ed me then got tired of me said i wasnt her type anymore i
was possesive and controlling
AOLiza: perhaps i already know you were possesive and controlling.
five: i was huh you nuts i gave her all the room she wanted never told nhewre what to do or
where to go
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Acting humanly: The Turing Test approach
A computer passes the test if a human interrogator, after posing some written questions, cannot tell
whether the written responses come from a person or from a computer
The computer would need to possess the following capabilities:
1. natural language processing to enable it to communicate successfully in English
2. knowledge representation to store what it knows or hears
3. automated reasoning to use the stored information to answer questions and to draw new
conclusions
4. machine learning to adapt to new circumstances and to detect and extrapolate patterns
5. TOTAL TURING TEST- To pass the total Turing Test, the computer will need
computer vision to perceive objects, and
robotics to manipulate objects and move about
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Thinking humanly: The cognitive modeling
approach
introspection—trying to catch our own thoughts as they go
by
psychological experiments—observing a person in action
brain imaging—observing the brain in action
cognitive science brings together computer models from AI
and experimental techniques from psychology to construct
precise and testable theories of the human mind.
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Thinking rationally: The “laws of thought” approach
SYLLOGISM: an instance of a form of reasoning in which a conclusion is
drawn from two given or assumed propositions, “Socrates is a man; all
men are mortal; therefore, Socrates is mortal.”
LOGIC: study of laws of thought to govern the operation of the mind is not
easy to take informal knowledge and state it in the formal terms required
e by
logical notation
Even problems with just a few hundred facts can exhaust the
computational resources of any computer unless it has some guidance as
to which reasoning steps to try first.
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Acting rationally: The rational agent approach
An agent is just something that acts
Rational behavior is doing the right thing
Right thing is expected to maximize goal achievement, given available information
computer agents
◦ operate autonomously,
◦ perceive their environment,
◦ persist over a prolonged time period,
◦ adapt to change, and
◦ create and pursue goals
Rational agent is one that acts so as to achieve the best outcome or, when there is
uncertainty, the best expected outcome
correct inference is not all of rationality in some situations, there is no provably
correct thing to do, but something must still be done. There are also ways of
acting rationally that cannot be said to involve inference
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REQUIREMENTS
NATURAL LANGUAGE PROCESSING
To enable it to communicate successfully
KNOWLEDGE REPRESENTATION
Knowledge representation to store what it knows or hears;
AUTMATED REASONING
Automated reasoning to use the stored information to answer questions and to draw new conclusions
MACHINE LEARNING
machine learning to adapt to new circumstances and to detect and extrapolate patterns.
COMPUTER VISION
Computer vision to perceive objects
ROBOTICS
Robotics to manipulate objects and move about
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How do we measure if Artificial Intelligence is
acting like a human?
Turing Test
The Cognitive Modelling
Approach
The Law of Thought Approach
The Rational Agent Approach
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Artificial Intelligence
An intelligent entity created by humans.
Capable of performing tasks intelligently without being explicitly
instructed.
Capable of thinking and acting rationally and humanely.
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Fields in AI
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THE FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
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Philosophy
• Can formal rules be used to draw valid conclusions?
• How does the mind arise from a physical brain?
• Where does knowledge come from?
• How does knowledge lead to action?
Rationalism: power of reasoning in understanding the world.
Dualism: there is a part of the human mind (or soul or spirit) that is outside of
nature, exempt from physical laws.
Materialism: brain’s operation according to the laws of physics constitutes the
mind.
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Philosophy
Empiricism: is the idea that all learning comes from only experience and
observations.
Induction: general rules are acquired by exposure to repeated associations
between their elements
Logical positivism: doctrine holds that all knowledge can be characterized by
logical
theories connected, ultimately, to observation sentences that correspond to
sensory inputs; thus logical positivism combines rationalism and empiricism
confirmation theory: attempted to analyze the acquisition of knowledge from
experience
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Mathematics
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
• Three fundamental areas:
1. Logic
2. Computation
3. Probability
George Boole: worked out the details of
propositional or Boolean, logic.
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Gottlob Frege: creating the first order logic that is used today.
Euclid’s algorithm: first nontrivial algorithm.
Kurt G¨odel: incompleteness theorem.
Alan Turing: characterize exactly which functions are computable.
Tractability: problem is called intractable if the time required to solve
instances of the problem grows exponentially with the size of the
instances.
Theory of probability: deal with uncertain measurements and
incomplete theories.
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Economics
• 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?
studying how people make choices that lead to preferred outcomes
Decision theory: combines probability theory with utility theory, provides a
formal and complete framework for decisions made under uncertainty.
Game theory: Von Neumann and Morgenstern, a rational agent should adopt
policies that are (or least appear to be) randomized. Game theory does not offer
an unambiguous prescription for selecting actions.
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Neuroscience
• How do brains process information?
Neuroscience is the study of the nervous system, particularly the brain Aristotle
wrote, “Of all the animals, man has the largest brain in proportion to his size.”
Nicolas Rashevsky: the first to apply mathematical models to the study of the
nervous system.
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Neuroscience
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Psychology
• How do humans and animals think and act?
• The study of the mental processes of an Artificial Intelligence
System (AIS) similar to humans
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Computer Engineering
• How can we build an efficient computer?
• Computer engineers design and develop algorithms that underpin AI
systems. They create mathematical models, statistical techniques, and
machine learning algorithms to analyze data and make informed
decisions.
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Control theory and cybernetics
• How can artifacts operate under their own control?
• Control theory and cybernetics are related fields that are used in artificial
intelligence (AI) to help machines interact with their environment and make
decisions.
• Control theory is the study of how agents can best interact with their environment
to achieve a desired goal.
• Cybernetics: It looks at the interactions between parts of complex systems through
processes like feedback and communication.
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Linguistics
How does language relate to thought?
Linguistic artificial intelligence (AI) is a specialized branch of AI dedicated
to enabling computers to understand, process, and generate human
language, making interactions with technology more natural and intuitive.
Linguistic intelligence in AI is concerned with enabling machines to
gracefully comprehend and create human language that is both situational
and accurate.
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History of AI
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Founding Fathers
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Gestation of Artificial Intelligence (1943- 1955)
•Year 1943: The first work which is now recognized as AI was done by
Warren McCulloch and Walter pits in 1943. They proposed a model of
artificial neurons.
•Year 1949: Donald Hebb demonstrated an updating rule for modifying the
connection strength between neurons. His rule is now called Hebbian
learning.
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Gestation of Artificial Intelligence (1943- 1955)
•Year 1950: The Alan Turing who was an English mathematician and pioneered Machine
learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in
which he proposed a test. The test can check the machine's ability to exhibit intelligent
behavior equivalent to human intelligence, called a Turing test.
Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial
intelligence program Which was named as "Logic Theorist". This program had proved
38 of 52 Mathematics theorems, and find new and more elegant proofs for some
theorems.
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The birth of artificial intelligence (1956)
The word "Artificial Intelligence" first adopted by American Computer scientist John
McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic
field.
The golden years-Early enthusiasm (1956-1974)
•Year 1966:
• The researchers emphasized developing algorithms which can solve mathematical
problems. Joseph Weizenbaum created the first chatbot in 1966, which was named
as ELIZA.
•Year 1972:
• The first intelligent humanoid robot was built in Japan which was named as
WABOT-1.
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Deep Networks-A Brief history
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Intelligent Agents: Agents and Environments
• An AI system is composed of an agent and its environment. The agents act in their environment.
The environment may contain other agents.
• An agent can be anything that perceive its environment through sensors and act upon that
environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting.
An agent can be:
• Human-Agent: A human agent has eyes, ears, and other organs which work for sensors and hand,
legs, vocal tract work for actuators.
• Robotic Agent: A robotic agent can have cameras, infrared range finder, NLP for sensors and
various motors for actuators.
• Software Agent: Software agent can have keystrokes, file contents as sensory input and act on
those inputs and display output on the screen.
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Intelligent Agents: Agents and Environments
Percept:
We use the term percept to refer to the agent's
perceptual inputs at any given instant.
Percept Sequence:
An agent's percept sequence is the complete Fig : Agents and Environments
history of everything the agent has ever perceived.
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Intelligent Agents: Agents and Environments
Agent function:
Mathematically speaking, we say that an agent's behavior is described by
the agent function that maps any given percept sequence to an action.
Agent program
Internally, the agent function for an artificial agent will be implemented
by an agent program. It is important to keep these two ideas distinct.
The agent function is an abstract mathematical description; the agent
program is a concrete implementation, running on the agent architecture.
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Intelligent Agents: Agents and Environments
The agent function maps from percept histories to
actions: f : P ∗ → A
The agent program runs on the physical architecture
to produce f.
Vacuum-cleaner world
Percepts: location and contents, e.g., [A,Dirty]
• Actions: Left, Right, Suck, NoOp
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Intelligent Agents: Agents and Environments
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Intelligent Agents: Agents and Environments
Function REFLEX-VACCUM-AGENT ([location, status])
returns an action If status=Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
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Good Behavior: Rationality
• A rational agent is one that “does the right thing”, i.e. the table for the agent
function is filled out “correctly.”
• But what does it mean to do the right thing? We use a performance measure to
evaluate any given sequence of environment states.
• Importantly, we emphasize that the performance is assessed in terms of
environment states and not agent states; self-assessment is often susceptible to self-
delusion.
• Here is a relevant rule of thumb: It is advisable to design performance measures
according to what one actually wants in the environment, as opposed to how one
believes that agent should behave.
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Rationality What is rational at any given time depends on (at least) four
things:
(1) The performance measure
(2) The agent’s prior knowledge
(3) The actions the agents can perform
(4) The agent’s percept sequence to date.
Definition of a rational agent: For each possible precept sequence, a rational
agent should select an action that is expected to maximize its performance
measure, given the evidence provided by the percept sequence and whatever
built-in knowledge the agent possesses.
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An intelligent agent should not only gather information, but also learn.
• The agent’s initial configuration could reflect some prior knowledge of the
environment, but as the agent gains experience, this may be modified and
augmented (an extreme case is when the environment is known a priori).
• Generally speaking, a rational agent should be autonomous, in the sense that
it learns what it can to compensate for partial or incorrect prior knowledge.
After sufficient experience of its environment, the behavior of a rational agent
can become effectively independent of its prior knowledge.
• Ideally, the incorporation of learning allows for the design of a single
rational agent that will succeed in a variety of different environments and for a
variety of tasks (the goal of AGI).
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The Nature of Environments
PEAS To design a rational agent, we must specify the task environment.
Consider, e.g., the task of designing an automated taxi:
Performance measure??
Environment??
Actuators??
Sensors??
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PEAS To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing an automated taxi:
Performance measure safety, destination, profits, legality, comfort, ...
Environment US streets/freeways, traffic, pedestrians, weather, ...
Actuators steering, accelerator, brake, horn, speaker/display, ...
Sensors video, accelerometers, gauges, engine sensors, keyboard, GPS, ...
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PEAS Agent: Medical diagnosis system
Performance measure: Healthy patient, minimize costs, lawsuits
Environment: Patient, hospital, staff
Actuators: Screen display (questions, tests, diagnoses, treatments, referrals)
Sensors: Keyboard (entry of symptoms, findings, patient's answers)
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Fig: PEAS description of the task environment for an automated taxi driver
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Environment types
• Fully observable (vs. partially observable): An agent's sensors give it
access to the complete state of the environment at each point in
time.
• Deterministic (vs. stochastic): The next state of the environment is
completely determined by the current state and the action executed
by the agent. (If the environment is deterministic except for the
actions of other agents, then the environment is strategic).
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Environment types
• Episodic (vs. sequential): The agent's experience is divided into
atomic "episodes" (each episode consists of the agent perceiving
and then performing a single action), and the choice of action in
each episode depends only on the episode itself.
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Environment types
• Static (vs. dynamic): The environment is unchanged while an agent is
deliberating. (The environment is semi dynamic if the environment itself does
not change with the passage of time but the agent's performance score does)
• Discrete (vs. continuous): A limited number of distinct, clearly defined
percepts and actions.
• Single agent (vs. multiagent): An agent operating by itself in an
environment.
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Environment types
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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The Structure of Agents
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