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1



Jimma

University,JiT

Depatment

of Computing

Introduction To Artificial Intelligence


Zelalem

H.

2

Outline

(1)Introduction

•
What is AI?

•
Foundations of AI

•
State of the art in AI

•
History of AI (Reading assignment)

(2) Intelligent Agents

•
Agents and environments

•
Rationality

•
The Nature of environments

•
The structure of agents


3


1. Introduction


For thousands of years, we have tried to
understand
how we think
!



AI, goes further still; it attempts not just to
understand but also to
build

intelligent
entities.


4

Introduction…

What is AI?

Some possible definitions


•
Thinking humanly
Thinking rationally

•
Acting humanly

Acting rationally



5

Introduction…

Thinking humanly

•
Cognitive science
:
the brain as an information
processing machine

•
Requires scientific theories of how the brain works

•

How to understand cognition as a computational
process?

•
Introspection: try to think about how we think

•
Predict and test behavior of human subjects

•
Image the brain, examine neurological data


6

Acting humanly

•
The Turing Test





•
What capabilities would a computer need to have to pass
the Turing Test?

•
Natural language processing

•
Knowledge representation

•
Automated reasoning

•
Machine learning

Introduction…

7

Turing Test: Criticism

•
What are some potential problems with the Turing Test?

•
Some human behavior is not intelligent

•
Some intelligent behavior may not be human

•
Human observers may be easy to fool


•
Chinese room argument
: one may simulate intelligence without
having true intelligence


•
Is passing the Turing test a good scientific goal?

Introduction…

8

Introduction…

Thinking rationally

•
Idealized or “right” way of thinking

•
Logic:

patterns of argument that always yield correct
conclusions when supplied with correct premises


“Socrates is a man;


All men are mortal;


Therefore Socrates is mortal.”

•
Beginning with Aristotle, philosophers and mathematicians
have attempted to formalize the rules of logical thought


9

Introduction…

Thinking rationally …

•
Logicist

approach to AI:
describe problem in formal
logical notation and apply general deduction
procedures to solve it

•
Problems with the
logicist

approach

•
Computational complexity of finding the solution

•
Describing real
-
world problems and knowledge in logical
notation

•
A lot of intelligent or “rational” behavior has nothing to do
with logic


10

Introduction…

Acting rationally

•
A
rational agent
is one that acts to achieve the best
outcome

•
Goals are application
-
dependent and are expressed in terms
of the
utility of outcomes

•
Being rational means
maximizing your expected utility

•
This definition of rationality only concerns the
decisions/actions that are made, not the cognitive
process behind them

11

Introduction…

Acting rationally…

•
Advantages

•
Generality:

goes beyond explicit reasoning, and even
human cognition altogether

•
Practicality:

can be adapted to many real
-
world problems

•
Amenable

to good scientific and engineering methodology

•
Avoids philosophy and psychology

•
Any disadvantages?

•
Not feasible in complicated
envt’s


•
Computational demands are just too high

12

AI Foundations

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?


Mathematics

•
What are the formal rules to draw valid conclusions?

•
What can be computed?

•
How do we reason with uncertain information?

13

AI Foundations…

Economics

•
How should we make decisions so as to maximize
payoff?

•
How should we do this when others may not go
along?


Neuroscience


How do brains process information?

Psychology


How do humans and animals think and act?

14

AI Foundations…

Computer Engineering


How can we build an efficient computer?

Control Theory and Cybernetics


How can artifacts operate under their own
control?

Linguistics


How does language relate to thought?


15

The state
-
of
-
the
-
art



Robotic Vehicles


Speech recognition


Autonomous planning and scheduling


Game playing


Spam fighting, fraud detection


Robotics


Machine translation


Vision


16

The History of Artificial intelligence


Reading Assignment

From Page 16 to 28


Russell and
Norvig
, 3
rd

edition

17

2. Intelligent Agents

An
agent

is anything that can be viewed as
perceiving

its
environment

through
sensors

and
acting

upon
that environment through
actuators

18

Agent function

The
agent

function

maps from
percept histories
to
actions


The
agent

program

runs on the physical
architecture

to produce
the agent function


agent = architecture +
program

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Vacuum
-
cleaner world

Percepts
:



Location
and
statu
s,


e.g
.,
[
A,Dirty
]

Actions:



Left
, Right, Suck,
NoOp


function Vacuum
-
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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Rational agents

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
the agent’s built
-
in knowledge


Performance measure

An
objective

criterion for success of an agent's
behavior


21

Specifying the task environment

Problem specification:
Performance measure,
Environment, Actuators,
Sensors (PEAS)

Example:
automated taxi
driver

•
Performance measure

–
Safe, fast, legal, comfortable trip, maximize
profits

•
Environment

–
Roads, other traffic, pedestrians, customers

•
Actuators

–
Steering wheel, accelerator, brake, signal, horn

•
Sensors

–
Cameras, sonar, speedometer, GPS,
odometer, engine sensors, keyboard

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Agent: Part
-
sorting robot

Performance measure

•
Percentage
of parts in correct bins

Environment

•
Conveyor
belt with parts, bins

Actuators

•
Robotic arm

Sensors

•
Camera
, joint angle sensors

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Agent: Spam filter

Performance measure

•
Minimizing false positives, false negatives

Environment

•
A user’s email account

Actuators

•
Mark as spam, delete, etc.

Sensors

•
Incoming messages, other information about user’s account

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Environment types

Fully observable
(vs
. partially
observable):
The

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 agent’s action

Episodic
(vs. sequential):

The
agent's experience is divided into atomic
“
episodes,”
and the choice of action in each episode depends only on
the episode
itself

26

Environment types

Static (vs. dynamic):


The
environment is unchanged while an agent is
deliberating

•
Semidynamic
:

the environment does
not change with the passage
of
time,
but the agent's performance score
does

Discrete (vs. continuous):


The environment provides a fixed
number of
distinct percepts,
actions, and environment states

•
Time can also evolve in a discrete or continuous fashion


27

Environment types


Single agent (vs. multi
-
agent):
An agent
operating by itself in an environment


Known (vs. unknown):
The agent knows the
rules of the environment


28

Examples





Chess w Clock without clock Taxi

Fully observable

Deterministic

Episodic

Static

Discrete

Single Agent



29

Yes

Yes

No

Strategic

Strategic

No

No

No

No

Semi

Yes

No

Yes

Yes

No

No

No

No

Hierarchy of agent
types



Simple
reflex
agents


Model
-
based
reflex
agents


Goal
-
based agents


Utility
-
based
agents

30

Simple reflex
agent

Select action on the basis of current percept,
ignoring all past percepts

31

Model
-
based reflex
agent

Maintains internal state that keeps track of
aspects of the environment that cannot be
currently observed

32

Goal
-
based
agent

The agent uses goal information to select
between possible actions in the current state

33

Utility
-
based
agent

The agent uses a utility function to evaluate the
desirability of states that could result from each
possible action


34

A learning agent



to build learning machines and then to teach

them.

35

A learning Agent

learning element
, which is responsible for making
improvements,


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.

problem generator
: responsible for suggesting actions that will
lead to new and informative experiences



Questions?