Artificial intelligence BCA 6th Sem Notes

ARTIFICIAL INTELLIGENCE
Presenter:NITHIN.H
ST.JOSEPH’S DEGREE COLLEGE, SOMWARPET

The Foundations of Artificial Intelligence
Philosophy (428 )
Mathematics (c. 800)
Economics (1776)
Neuroscience (1861)
Psychology (1879)
Computer engineering (1940)

Philosophy
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?
foundational issues (can a machine think?), issues of knowledge and believe, mutual knowledge
•Philosophers staked out most of the important ideas of k1, but the leap to a formal science required
•a level of mathematical formalization in three fundamt:ntal areas: logic, computation,
•and probability.
•Mathematics (c. 800-present)
•o What are the formal rules to draw valid conclusi~ons?
•What can be computed?
•e How do we reason with uncertain information?

Economics (1776-present)
a How should we make decisions so as to maximize payoff?
o How should we do this when others may not go along?
a How should we do this when the payoff may be f,x in the future?
•Neuroscience (1861-present)
•How do brains process information?
Psychology (1879-present)
a How do humans and animals think and act?
Computer engineering (1940-present)
How can we build an efficient computer?
complexity theory, algorithms, logic and inference, programming languages, and system
building.

•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.
•oYear 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.
History Artificial Intelligence (1943-1952)

The birth of Artificial Intelligence (1952-1956)
•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.
•Year 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.
At that time high-level computer languages such as FORTRAN, LISP, or
COBOL were invented. And the enthusiasm for AI was very high at that
time.

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.
A boom of AI (1980-1987)
• Year 1980: After AI winter duration, AI came back with "Expert System".
Expert systems were programmed that emulate the decision-making ability of
a human expert.

The emergence of intelligent agents (1993-2011)
•Year 1997: In the year 1997, IBM Deep Blue beats world chess
champion, Gary Kasparov, and became the first computer to beat a
world chess champion.
•Year 2002: for the first time, AI entered the home in the form of
Roomba, a vacuum cleaner.
•Year 2006: AI came in the Business world till the year 2006.
Companies like Facebook, Twitter, and Netflix also started using AI.

Deep learning, big data and artificial general intelligence (2011-present)
•Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it
had to solve the complex questions as well as riddles. Watson had proved that it
could understand natural language and can solve tricky questions quickly.
•Year 2012: Google has launched an Android app feature "Google now", which was
able to provide information to the user as a prediction.
•Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in
the infamous "Turing test."
•Year 2018: The "Project Debater" from IBM debated on complex topics with two
master debaters and also performed extremely well.

•Google has demonstrated an AI program "Duplex" which was a virtual
assistant and which had taken hairdresser appointment on call, and lady on
other side didn't notice that she was talking with the machine.
Now AI has developed to a remarkable level.
• The concept of Deep learning, big data, and data science are now trending
like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon
are working with AI and creating amazing devices. The future of Artificial
Intelligence is inspiring and will come with high intelligence.

An agent is anything that can be viewed as perceiving its environment through
sensors and sensor acting upon that environment through actuators.
Agents and environments
1. A human agent has eyes, ears, and other organs for sensors and hands, legs,
mouth, and other body parts for actuators.
2. A robotic agent might have cameras and infrared range finders for sensors and
various motors for actuators.
3. A software agent receives keystrokes, file contents, and network packets as
sensory inputs and acts on the environment by displaying on the screen, writing
files, and sending network packets.

Sensor: Sensor is a device which detects the change in the environment and
sends the information to other electronic devices. An agent observes its
environment through sensors.
Actuators: Actuators are the component of machines that converts energy into
motion. The actuators are only responsible for moving and controlling a system.
An actuator can be an electric motor, gears, rails, etc.
Effectors: Effectors are the devices which affect the environment. Effectors can
be legs, wheels, arms, fingers, wings, fins, and display screen.

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
1. The agent function for an artificial agent will be implemented by an agent program.
2. It is important to keep these two ideas distinct.
3. The agent function is an abstract mathematical description;
4. the agent program is a concrete implementation, running on the agent architecture.
5. To illustrate these ideas, we will use a very simple example-the vacuum-cleaner world
shown in Figure.
6. This particular world has just two locations: squares A and B.
7. The vacuum agent perceives which square it is in and whether there is dirt in square.
8. It can choose to move left, move right, suck up the dirt, or do nothing.
9. One very simple agent function is the following:
10. if the current square is dirty, then suck, otherwise,
11. it move to the other square.

Percept Sequence Action
[A, Clean] Right
[A, Dirty] Suck
[B, Clean] Left
[B, Dirty] Suck
[A, Clean], [A, Clean] Right
[A, Clean], [A, Dirty] Suck
….. …..
Agent program
function Reflex-VACUUM-AGENT ([locations,
status]) returns an action if status = Dirty then return
Suck
else if location = A then return Right

Intelligent Agents:
An intelligent agent is an autonomous entity which act upon an environment using
sensors and actuators for achieving goals. An intelligent agent may learn from the
environment to achieve their goals. A thermostat is an example of an intelligent
agent.
Following are the main four rules for an AI agent:
o Rule 1: An AI agent must have the ability to perceive the environment.
o Rule 2: The observation must be used to make decisions.
o Rule 3: Decision should result in an action.
o Rule 4: The action taken by an AI agent must be a rational action.

The task of AI is to design an agent program which implements the agent function.
The structure of an intelligent agent is a combination of architecture and agent
program. It can be viewed as:
Agent = Architecture + Agent program
Following are the main three terms involved in the structure of an AI
agent:
Architecture: Architecture is machinery that an AI agent executes on.
Agent Function: Agent function is used to map a percept to an action.
Agent program: Agent program is an implementation of agent function. An
agent program executes on the physical architecture to produce function f.

Rational Agent:
A rational agent is an agent which has clear preference, models uncertainty, and
acts in a way to maximize its performance measure with all possible actions
Performance measures
1. A performance measure embodies the criterion for success of an agent's
behavior.
2. When an agent is plunked down in an environment, it generates a
sequence of actions according to the percepts it receives.
3. This sequence of actions causes the environment to go through a sequence
of states.
4. If the sequence is desirable, then the agent has performed well

Specifying the task environment
PEAS Representation
PEAS is a type of model on which an AI agent works upon. When we define an AI
agent or rational agent, then we can group its properties under PEAS representation
model. It is made up of four words:
o P: Performance measure
o E: Environment
o A: Actuators
o S: Sensors

1) Self Driving car
Performance: Safety, time, legal drive, comfort Environment: Roads, other vehicles,
road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn
Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.

Types of Agents
Agents can be grouped into four classes based
on their degree of perceived intelligence and
capability :
• Simple Reflex Agents
• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Learning Agent

Problems with Simple reflex agents are :
• Very limited intelligence.
• No knowledge of non-perceptual parts of the state.
• Usually too big to generate and store.
• If there occurs any change in the environment, then the collection of rules need to
be updated.
Simple reflex agents
Simple reflex agents ignore the rest of the percept history and act only on
the basis of the current percept. Percept history is the history of all that
an agent has perceived to date. The agent function is based on the
condition-action rule.

Model-based reflex agents
It works by finding a rule whose condition matches the current situation. A model-
based agent can handle partially observable environments by the use of a model
about the world. The agent has to keep track of the internal state which is adjusted by
each percept and that depends on the percept history.
Updating the state requires information about :
• how the world evolves independently from the agent, and
• how the agent’s actions affect the world.

Goal-based agents
These kinds of agents take decisions based on how far they are currently
from their goal(description of desirable situations). Their every action is
intended to reduce its distance from the goal. This allows the agent a way
to choose among multiple possibilities, selecting the one which reaches a
goal state.

Utility-based agents
The agents which are developed having their end uses as building blocks
are called utility-based agents. When there are multiple possible
alternatives, then to decide which one is best, utility-based agents are used.
They choose actions based on a preference (utility) for each state.

Learning Agent :
A learning agent in AI is the type of agent that can learn from its past
experiences or it has learning capabilities. It starts to act with basic knowledge
and then is able to act and adapt automatically through learning.
A learning agent has mainly four conceptual components, which are:
1. Learning element: It is responsible for making improvements by learning
from the environment
2. Critic: The learning element takes feedback from critics which describes how
well the agent is doing with respect to a fixed performance standard.
3. Performance element: It is responsible for selecting external action
4. Problem Generator: This component is responsible for suggesting actions that
will lead to new and informative experiences.

Artificial intelligence BCA 6th Sem Notes

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