Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCE

Created By-
Khushboo Pal
B.Tech (Computer Science & Engineering)

Instructional Objectives
Define an agent.
Agents Classification.
Define an Intelligent agent.
Define a Rational agent.
Explain classes or Types of
intelligent agents
Applications of Intelligent agent

Agents
 An agent is anything that can be viewed as
perceiving its environment through sensors and
acting upon that environment through effectors.
 A human agent has eyes, ears, and other organs for
sensors, and hands, legs, mouth, and other body
parts for effectors/actuators.
 A robotic agent substitutes cameras and infrared
range finders for the sensors and various motors for
the effectors.

Agents
 Operate in an environment.
 Perceives and acts upon it's environment
through actuators/sensors and have its goals.
.

Sensors & Effectors
 An agent Perceives its environment through
sensors.
 The complete set of inputs at a given time is called
percept.
 The current percept, or a sequence of percepts can
influence the actions of an agent.
 It can change the environment through
effectors.
 An operation involving an actuator is called
an action ,which can be grouped in to action
sequences.

Examples of agents
 Humans
eyes, ears, skin, taste buds, etc. for Sensors.
hands, fingers, legs, mouth for effectors.
etc. for
 Robots
camera, infrared, bumper, etc. for sensors.
grippers, wheels, lights, speakers, effectors.

Structure of agents
 A simple agent program can be defined
mathematically as an agent function which
maps every possible precepts sequence to a
possible action the agent can perform.
F: p*-> A
 the term percept is use to the agent's
perceptional inputs at any given instant.

Intelligent agents
 Fundamental functionalities of
intelligence Acting are:
Sensing
Understanding, Reasoning, learning
 In order to act you must sense. Blind actions is not
a characterization of intelligence.
 Robotics: sensing and acting.
Understanding not necessary.
 Sensing needs understanding to be useful.

Intelligent Agents
IntelligentAgent:
 must sense,
 must act,
 must be rational,
and autonomous.

Rational Agent
 AI is about building rational agents.
 An agent is something that perceives and
acts.
 A rational agent always does the right
thing as-
What are the Functionalities ?(Goals)
What are the components?
How do we build them?

Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCE

Rationality
 Perfect Rationality:
Assumes that the rational agent knows
all and will take the action that maximize
the utility.
Human beings do not satisfy this
definition of rationality.

Agent Environment
 Environments in which agents operate
can be defined in different ways.
 It is helpful to view the following
definitions as referring to the way the
environment appears from the point of
view of the agent itself.

Classes of Intelligent
Agents
 Intelligent agents are grouped in to five
classes based on their degree of perceived
intelligence and capability.
 Simple reflex agents
 Model based reflex agents
 Goal based agents
 Utility based agents
 Learning agents

1.Simple reflex agents
 Simple reflex agents act only on the basis of the
current percept, ignoring the rest of the percept
history. The agent function is based on the condition-
action rule: if condition then action.
 Succeeds when the environment is fully observable.
 Some reflex agents can also contain information on
their current state which allows them to disregard
conditions.

2. Model based reflex
agents
 A model-based agent can
handle a partially observable
environment.
 This knowledge about "how the world
evolves" is called a model of the world,
hence the name "model-based agent".

3.Goal based agents
 Goal-based agents further expand on the
capabilities of the model-based agents, by using
"goal" information.
 Goal information describes situations that are
desirable. This allows the agent a way to choose
among multiple possibilities, selecting the one
which reaches a goal state.
 Search and planning are the subfields of artificial
intelligence devoted to finding action sequences
that achieve the agent's goals.

4. Utility based agents
 Goal-based agents only distinguish between goal states
and non-goal states.
 It is possible to define a measure of how desirable a
particular state is. This measure can be obtained through
the use of a utility function which maps a state to a
measure of the utility of the state.
 A more general performance measure should allow a
comparison of different world states according to exactly
how happy they would make the agent. The term utility,
can be used to describe how "happy" the agent is.

5. Learning agents
 Learning has an advantage that it allows the agents to
initially operate in unknown environments and to become
more competent than its initial knowledge alone might
allow.
 The most important distinction is between the "learning
element", which is responsible for making improvements,
and the "performance element", which is responsible for
selecting external actions.
 The learning element uses feedback from the "critic" on
how the agent is doing and determines how the
performance element should be modified to do better in the
future.

Learning agents
 The last component of the learning agent is
the "problem generator". It is responsible for
suggesting actions that will lead to new and
informative experiences.

Applications of
Intelligent Agents
 Intelligent agents are applied as
automated online assistants, as
Where they function to perceive the needs of
Customers in order to perform individualized
customer service.
 Use in smart phones in future.

Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCE

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