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
19
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
20
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
23
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
24
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
25
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?
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