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01/02/2023
John McCarthy (1927-2011)
In 1956, John organized the mythic Dartmouth conference where, in his talk,
he first coined the term “artificial intelligence”.
According to the father of Artificial Intelligence, John McCarthy, AI is The science and engineering of
making intelligent machines, especially intelligent programs.
Can we make the machine intelligent?
Humans are best at understanding, reasoning, and interpreting knowledge.
Human knows things, which is knowledge and as per their knowledge they
perform various actions in the real world.
But how machines do all these things comes under knowledge
representation and reasoning.
Hence we can describe Knowledge representation as follows:
• Knowledge representation and reasoning are part of Artificial intelligence which is
concerned with AI agents thinking and how thinking contributes to the intelligent behavior
of agents.
• It is responsible for representing information about the real world so that a computer can
understand and can utilize this knowledge to solve complex real-world problems such as
the diagnosis of a medical condition or communicating with humans in natural language.
• It is also a way which describes how we can represent knowledge in artificial intelligence.
Knowledge representation is not just storing data in some database, but it also enables
an intelligent machine to learn from that knowledge and experiences so that it can
behave intelligently like a human.
Following is the kind of knowledge that needs to be represented in AI
systems:
• Object: All the facts about objects in our world domain. E.g., Guitars contain
strings, and trumpets are brass instruments…………..
• Events: Events are the actions that occur in our world.
• Performance: It describes behavior that involves knowledge about how to do
things.
• Meta-knowledge: It is knowledge about what we know.
• Facts: Facts are the truths about the real world and what we represent.
• Knowledge-Base: The central component of the knowledge-based agents is the
knowledge base. It is represented as KB. The Knowledge base is a group of
Sentences (Here, sentences are used as a technical term and not identical to the
English language).
Declarative Knowledge:
•Declarative knowledge is to know about something.
•It includes concepts, facts, and objects.
•It is also called descriptive knowledge and expressed in declarative sentences.
•It is simpler than procedural language.
Procedural Knowledge:
•It is also known as imperative knowledge.
•Procedural knowledge is a type of knowledge which is responsible for knowing how
to do something.
•It can be directly applied to any task.
•It includes rules, strategies, procedures, agendas, etc.
•Procedural knowledge depends on the task on which it can be applied.
Meta-knowledge:
•Knowledge about the other types of knowledge is called Meta-knowledge.
Heuristic knowledge:
•Heuristic knowledge is representing the knowledge of some experts in a field or
subject.
•Heuristic knowledge is rules of thumb based on previous experiences,
awareness of approaches, and which are good to work but not guaranteed.
Structural knowledge:
•Structural knowledge is basic knowledge for problem-solving.
•It describes relationships between various concepts such as kind of, part of,
and grouping of something.
•It describes the relationship that exists between concepts or objects.
The relation between knowledge and intelligence:
Knowledge of real-worlds plays a vital role in intelligence and same for creating
artificial intelligence. Knowledge plays an important role in demonstrating
intelligent behavior in AI agents. An agent is only able to accurately act on some
input when he has some knowledge or experience about that input.
Decision-maker acts by sensing the environment and using the knowledge. But if the
knowledge part is not present then, it cannot display intelligent behavior.
How AI system can interact with the real worl
AI system has Perception component by which it retrieves information from its environment. It can be visual, audio or another form
of sensory input.
The learning component is responsible for learning from data captured by Perception comportment.
Knowledge representation and Reasoning
are involved in showing the intelligence in machine-like humans.
The planning and execution depend on analysis of Knowledge representation and reasoning.
What is Problem?
In general, a problem is an obstacle that makes it
troublesome/difficult to attain a desired goal, objective, or
purpose.
In a broader sense, a problem exists once an individual
become tuned in to important distinction between what really
is and what is desired.
Each problem has an answer or solution.
What is a Solution?
A procedure that makes the problem navigation towards the goal is
called solution.
It can be an algorithm or a hard-core implementation to achieve the
goal defined by the problem.
Types of Problem and Solution
Depending on the way in which the problems are solved,
problems are classified into the following types:
1. Structured problem
2. Unstructured problem
Structured problem
Structural problems are the ones for which there exists a specific
algorithm to achieve the goal. The same algorithm is run against
variety input data still giving a guarantee of the problem being
solved.
Since the structure of the solution (that is in the algorithm) remains
the same, even if the input data changes, these problems are called
structured problems.
General solutionstructured problem
Unstructured problem
Unstructured problems are the problems for which there does not exist a
specific algorithm to achieve the goal. What step to take to achieve the goal
depends on what is the current state of the problem.
AI is an attempt to make a computer to solve unstructured problems.
A general solution to an unstructured problem
In 1950 Alan Turing published a paper in which he speculated about the possibility of
creating machines that think.
He noted that "thinking" is difficult to define and devised his famous Turing Test.
If a machine could carry on a conversation (over a teleprinter) that was
indistinguishable from a conversation with a human being, then it was reasonable to
say that the machine was "thinking".
The Turing Test was the first serious proposal in the philosophy of AI.
The Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or
not a computer is capable of thinking like a human being.
Goals of AI:
Scientific goal:
Scientific goal is to determine which ideas about knowledge
representation, learning, rule systems, search, and so on,
explain various sorts of real intelligence
Engineering goal:
Engineering goal is to solve real-world problems by using AI
techniques, such as knowledge representation, learning, rule
systems, search, and so on.
The scientific goal is to answer questions and discover information
about the chosen field of study, while an engineering goal focuses
on creating products or processes that solve problems.
Intelligent Agents
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Understand the concept of artificial intelligent agent
Understand the basic concept of rationality
Interpret and apply fundamental of type of agent on current problems
related to artificial intelligence.
Intelligent agent
An agent is defined something that sees (perceives) and acts in an
environment. IAs perform task that will be beneficial for the business
procedure, application or a person.
Agent terminology
Performance measure of agent: It is the criteria determining the success of
an agent.
⚫ Behavior/action of agent: It is the action performed by an agent after any
specified sequence of the percepts.
⚫ Percept: It is defined as an agent’s perceptual inputs at a specified instance.
⚫ Percept sequence: It is defined as the history of everything that an agent has
perceived till date.
⚫ Agent function: It is defined as a map from the precept sequence to an
action.
Agent function, a = F(p)
where p is the current percept, a is the action carried out, and F is the agent
function.
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Agent terminology
F maps precepts to actions
F:P→A
where P is the set of all precepts, and A is the set of all actions.
If an action is dependent on all the precepts observed, not only the current
percept,
ak = F ( p0 p1 p2 … pk )
Where p0, p1, p2, … ,pk is the sequence of percepts recorded till date, ak is the
resulting action carried out and F now maps percept sequences to action
F:P*→A
Structure of agent:
The structure of agent can be represented as:
Structure of Agent= Architecture + Agent
Architecture :
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Percepts the data from the sensor
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Runs the program
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Feed the actions to the effectors
PAGE- Intelligent Agent
While designing the intelligent systems, following four main factors to be
considered
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P, Percepts: The inputs to the AI system.
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A, Actions: The outputs of the AI system.
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G, Goals: What the agent is expected to achieve.
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E, Environment: What the agent is interacting with one.
Agent type
Percepts
Actions
Goals
Environment
Medical
diagnostic
system
Symptoms, test
results, patient’s
answers
Questions,
test requests,
treatments,
referrals
Healthy patients,
minimize the
costs
Patient, hospital,
staff
Satellite image
analysis system
Pixels of varying
intensity and
color
Display a
categorization of
the scene
Correct image
categorization
Images from
orbiting satellite
Part-picking
robot
Pixels of varying
intensity and
color
Pick up parts and
sort them into bins
Place parts into
correct bins
Conveyor belt
with parts, bins
Refinery
controller
Temperature,
pressure and
chemical readings
Open and close
valves, adjust
temperature
Maximize purity,
yield, safety
Refinery, staff
Interactive
Typed words
Display exercises,
Maximize
Set of students,
Attributes of agent:
Autonomy: Agent work without the direct interference of the people or others
and have some kind of control over their action and the internal state.
Social ability: Agent’s interface with different agents and human by the means or
specific likeness of agent communication language.
Reactivity: Agents perceive their condition which might include the physical
world, client by means of graphical user interface, an accumulation of agent
Proactivity: Agents do not simply act in response to their environment; but these
are able to exhibit goal–director behavior by taking initiative.
Goal orienteer: An agent is efficient of handling complex high-level tasks
Concept of Rationality:
Rationality: It is defined as the only status of being sensible.
Rational Agent:
 right action
 PEAS (performance measure, environment, actuators, sensors)
A system is rational if it does right things. [Russel and Norvig]
This gives four possible goals to peruse AI:
1. System that reasons (thinks) like human
2. System that reasons (thinks) rationally
3. System that acts like human
4. System that acts rationally
Rational agent
Rationality of an agent depends on mainly on the following four
parameters:
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The degree of success that is determined by the performance measures.
The percept sequence of the agent that have been perceived
Prior knowledge of the environment that is gained by an agent
The actions that the agent might perform in the environment.
Ideal rational agent:
An ideal rational agent is the one, which is competent enough of
performing expected actions to expand its performance measure, on the
basis of the following:
⚫ Its percept sequence.
⚫ Its built-in knowledge base.
Autonomy:
If the behaviour of the system is determined by its own experience, the
system is known as autonomous.
When the agent has very less experience, it will be required to act in a
random manner unless some assistance is provided either by the designer
or the programmer.