introduction to inteligent IntelligentAgent.ppt

• Agents and environments
• Rationality
• PEAS (Performance measure, Environment,
Actuators, Sensors)
• Class of Environment
• Agent types

Intelligent Agent
 I want to build a robot that will
 Clean my house
 Cook when I don’t want to
 Wash my clothes
 Cut my hair
 Fix my car (or take it to be fixed)
 Take a note when I am in a meeting
 Handle my emails (Information filtering agent)
i.e. do the things that I don’t feel like doing…
 AI is the science of building software or physical agents that act
rationally with respect to a goal.

Types of Intelligent Agents
 Software agents: also called a softbot (software robot)
 It is an agent that operates within the confines of the
computer or computer network.
 It interacts with a software environment by issuing
commands and interpreting the environments feedback.
 Softbots effectors are commands (e.g. mv or compress in
UNIX shell) to change the external environments state.
 E.g. mail handling agent, information filtering agent
 Physical agents
 are robots that have the ability to move and act in the
physical world and can perceive and manipulate objects in
that world, possibly for responding to new perceptions.

Agent
 Agent is something that perceives its environment
through SENSORS and acts upon that environment
through EFFECTORS.
 The agent is assumed to exist in an environment in which
it perceives and acts
 An agent is rational since it does the right thing to achieve
the specified goal.

Agent
Human Agent Physical Agent
Sensors Eyes, Ears,
Nose
Cameras, Scanners,
Mic, infrared range
finders
Effectors/
Actuators
Hands, Legs,
Mouth
Various Motors
(artificial hand,
artificial leg),
Speakers, Radio

How Agents should act?
 A rational agent should strive to "do the right thing",
based on what it can perceive and the actions it can
perform. The right action is the one that will cause the
agent to be most successful
 What does right thing mean? one that will cause the agent to
be most successful and is expected to maximize goal
achievement, given the available information
 A rational agent is not omniscient
 An Omniscient agent knows the actual outcome of its
actions, and can act accordingly, but in reality omniscience is
impossible.
 Rational agents take action with expected success, where as
omniscient agent take action with 100% sure of its success
 Are human beings Omniscient or Rational agent?

Example: Is the agent Rational?
 Alex was walking along the road to Bus Station; He saw an
old friend across the street. There was no traffic.
 So, being rational, he started to cross the street.
 Meanwhile a big banner falls off from above and before he
finished crossing the road, he was flattened.
Was Alex irrational to cross the street?
 This points out that rationality is concerned with expected
success, given what has been perceived.
Crossing the street was rational, because most of the
time, the crossing would be successful, and there was no
way you could have foreseen the falling banner.
The EXAMPLE shows that we can not blame an agent for
failing to take into account something it could not perceive.
Or for failing to take an action that it is incapable of taking.

Rational agent
 In summary what is rational at any given point depends on
PEAS (Performance measure, Environment, Actuators,
Sensors) framework.
 Performance measure
 The performance measure that defines degrees of success of the
agent
 Environment
 Knowledge: What an agent already knows about the
environment
 Actuators – generating actions
 The actions that the agent can perform back to the environment
 Sensors – receiving percepts
 Perception: Everything that the agent has perceived so far
concerning the current scenario in the environment
 For each possible percept 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 has.

Performance measure
 How do we decide whether an agent is successful or
not?
 Establish a standard of what it means to be successful
in an environment and use it to measure the
performance
 A rational agent should do whatever action is expected
to maximize its performance measure, on the basis of
the evidence provided by the percept sequence and
whatever built-in knowledge the agent has.
 What is the performance measure for “crossing the
road”?
 What about “Chess Playing”?

Example: PEAS
 Consider the task of designing an automated taxi
driver agent:
 Performance measure: Safe, fast, legal, comfortable
trip, maximize profits
 Environment: Roads, other traffic, pedestrians,
customers
 Actuators: Artificial legs & hands, Speaker
 Sensors: Cameras, GPS, engine sensors, recorder
(microphone)
 Goal: driving safely from source to destination point

Agent type Percepts Actions Goals Environment
Interactive
English
tutor
Typed words,
Keyboard
Print exercises,
suggestions,
corrections
Maximize
student's score
on test
Set of
students
Medical
diagnosis
system
Symptoms,
patient's
answers
Questions,
tests,
treatments
Healthy person,
minimize costs
Patient,
hospital
Part-
picking
robot
Pixels of
varying
intensity
Pick up parts
and sort into
bins
Place parts in
correct bins
Conveyor
belts with
parts
Satellite
image
analyser
Pixels of
varying
intensity, color
Print a
categorization
of scene
Correct
categorization
Images from
orbiting
satellite
Refinery
controller
Temperature,
pressure
readings
Open, close
valves; adjust
temperature
Maximize
purity, yield,
safety
Refinery
Examples: Agents for Various Applications

Assignment (for next class)
Consider the need to design a “player
agent” for the national team. It may be
chess player, football player, tennis
player, basket player, etc…
 Identify what to perceive, actions to take,
the environment it interacts with?
 Identify sensors, effectors, goals,
environment and performance measure
that should be integrated for the agent to be
successful in its operation?

Designing an agent
 A physical agent has two parts: architecture +
program
 Architecture
 Runs the programs
 Makes the percept from the sensors available to the
programs
 Feeds the program’s action choices to the effectors
 Programs
 Accepts percept from an environment and generates actions
 Before designing an agent program, we need to know the possible
percept and actions
 By enabling a learning mechanism, the agent could have a
degree of autonomy, such that it can reason and take
decision

function SKELETON-AGENT (percept) returns action
static: knowledge, the agent’s memory of the world
knowledge UPDATE-KNOWLEDGE(knowledge,percept)
action  SELECT-BEST-ACTION(knowledge)
knowledge UPDATE-KNOWLEDGE (knowledge, action)
return action
On each invocation, the agent’s knowledge base is updated to
reflect the new percept, the best action is chosen, and the fact
that the action taken is also stored in the knowledge base.
The knowledge base persists from one invocation to the next.
Program Skeleton of Agent
NOTE: Performance measure is not part of the agent

Classes of Environments
 Actions are done by the agent on the environment.
Environments provide percepts to an agent.
 Agent perceives and acts in an environment. Hence in
order to design a successful agent , the designer of the
agent has to understand the type of the environment it
interacts with.
 Properties of Environments:
 Fully observable vs. Partially observable
 Deterministic vs. Stochastic
 Episodic vs. Sequential
 Static vs. Dynamic
 Discrete vs. Continuous
 Single agent vs. Multiagent

Fully observable vs. partially observable
 Does the agent’s sensory see the complete state of
the environment?
 If an agent has access to the complete state of the
environment, then the environment is accessible or fully
observable.
 An environment is effectively accessible if the
sensors detect all aspects that are relevant to the
choice of action.
 Taxi driving is partially observable
 Any example of fully observable?

Deterministic vs. stochastic
 Is there a unique mapping from one state to
another state for a given action?
 The environment is deterministic if the next state
is completely determined by
 the current state of the environment and
 the actions selected and executed by the agent.
 Taxi driving is non-deterministic (i.e. stochastic)
 Any example of deterministic?

Episodic vs. Sequential
 Does the next “episode” or event depend on the
actions taken in previous episodes?
 In an episodic environment, the agent's experience
is divided into "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.
 The quality of its action depends just on the episode
itself.
 In sequential environment the current decision
could affect all future decisions
 Taxi driving is sequential
 Any example of Episodic?

Static vs. Dynamic
 Can the world change while the agent is thinking
and on purpose?
 If the environment can change while the agent is on
purpose, then we say the environment is dynamic for
that agent
 otherwise it is static.
 Taxi driving is dynamic
 Any example of static?

Discrete vs. Continuous
 Are the distinct percepts & actions limited or
unlimited?
 If there are a limited number of distinct, clearly
defined percepts and actions, we say the
environment is discrete.
 otherwise it is continuous.
 Taxi driving is continuous
 Any example of discrete? E.g playing chess

Single agent vs. Multiagent
 If an agent operate by itself in an environment, it
is a single agent environment.
 How do you decide whether another entity must be
viewed as an agent?
 Is it an agent or just a stochastically behaving object (ex: wave
on a beach)?
 Key question: can its behavior be described as
maximizing performance depending on the actions of
‘our’ agent?
 Classify multiagent environment as (partially)
competitive and/or (partially) cooperative
 Ex: Taxi is partially competitive and partially cooperative

Five types of agents
 Four basic types in order of increasing generality:
 Simple reflex agents
 Model-based reflex agents
 Goal-based agents
 Utility-based agents
 Learning agents

Simple reflex agents
 It works by finding a rule whose condition matches the
current situation (as defined by the percept) and then
doing the action associated with that rule.
E.g. If the car in front brakes, and its brake lights come
on, then the driver should notice this and initiate braking,
 Some processing is done on the visual input to establish
the condition. If "The car in front is braking"; then this
triggers some established connection in the agent program
to the action "initiate braking". We call such a connection
a condition-action rule written as: If car-in-front-is
breaking then initiate-braking.
 Humans also have many such conditions. Some of which
are learned responses. Some of which are innate
(inborn) responses
 Blinking when something approaches the eye.

Simple Reflex Agent sensors
What the world
is like now
What action I
should do now
Condition -
action rules
effectors
Environment
function SIMPLE-REFLEX-AGENT(percept) returns action
static: rules, a set of condition-action rules
state  INTERPRET-INPUT (percept)
rule  RULE-MATCH (state,rules)
action  RULE-ACTION [rule]
return action
Structure of a simple reflex agent

Model-Based Reflex Agent
 This is a reflex agent with internal state.
 It keeps track of the world that it can’t see now.
 It works by finding a rule whose condition matches the current
situation (as defined by the percept and the stored internal state)
 If the car is a recent model -- there is a centrally mounted brake
light. With older models, there is no centrally mounted, so what if
the agent gets confused?
 Is it a parking light? Is it a brake light? Is it a turn signal light?
 Some sort of internal state should be in order to choose an action.
 The camera should detect two red lights at the edge of the vehicle
go ON or OFF simultaneously.
 The driver should look in the rear-view mirror to check on the location
of near by vehicles. In order to decide on lane-change the driver needs
to know whether or not they are there.
 The driver sees, and there is already stored information, and then
does the action associated with that rule.

Structure of Model-Based reflex agent
sensors
What the world
is like now
What action I
should do now
Condition - action rules
effectors
Environment
State
How the world evolves
What my actions do
function REFLEX-AGENT-WITH-STATE (percept) returns action
static: state, a description of the current world state
rules, a set of condition-action rules
state  UPDATE-STATE (state, percept)
rule  RULE-MATCH (state, rules)
action  RULE-ACTION [rule]
state  UPDATE-STATE (state, action)
return action

Goal based agents
 Choose actions that achieve the goal (an agent with
explicit goals)
 Involves consideration of the future:
 Knowing about the current state of the environment is not
always enough to decide what to do.
 For example, at a road junction, the taxi can turn left,
right or go straight.
 The right decision depends on where the taxi is trying to get to.
As well as a current state description, the agent needs some sort
of goal information, which describes situations that are
desirable. E.g. being at the passenger's destination.
 The agent may need to consider long sequences, twists
and turns to find a way to achieve a goal.

sensors
What the world
is like now
What action I
should do now
Goals
effectors
Environment
State
What my actions do
What it will be like
if I do action A
Structure of a Goal-based agent
function GOAL_BASED_AGENT (percept) returns action
action  SELECT-ACTION [state, goal]
return action

Utility based agents
 Goals are not really enough to generate high quality
behavior.
 For e.g., there are many action sequences that will get
the taxi to its destination, thereby achieving the goal.
Some are quicker, safer, more reliable, or cheaper than
others. We need to consider Speed and safety
 When there are several goals that the agent can
aim for, none of which can be achieved with
certainty. Utility provides a way in which the
likelihood of success can be weighed up against the
importance of the goals.
 An agent that possesses an explicit utility function
can make rational decisions.

sensors
What the world is
like now
What action I should
do now
Utility
effectors
Environment
State
What my actions do
What it will be like
if I do action A
How happy I will be
in such as a state
Structure of a utility-based agent
function UTILITY_BASED_AGENT (percept) returns action
action  SELECT-OPTIMAL_ACTION [state, goal]
return action

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