Unit 1 Fundamentals of Artificial Intelligence-Part II.pptx

Sanjivani Rural Education Society’s
Sanjivani College of Engineering, Kopargaon-423603
(AnAutonomous Institute Affiliated to Savitribai Phule Pune University, Pune)
NAAC ‘A’GradeAccredited, ISO 9001:2015 Certified
Department of Information Technology
(NBA Accredited)
Department:- Information Technology
Name of Subject:- Artificial Intelligence
Class:- TYIT
Subject Code:- IT313
Sanjivani College of Engineering, Kopargaon Dept of Information Technology

Course Objectives:
1. To understand the basic principles of Artificial Intelligence
2. To provide an understanding of uninformed search strategies.
3. To provide an understanding of informed search strategies.
4. To study the concepts of Knowledge based system.
5. To learn and understand use of fuzzy logic and neural networks.
6. To learn and understand various application domain of Artificial Intelligence.
2

Production systems
3
• Production systems provide appropriate structures for
performing and describing search processes.
• A production system has four basic components as
enumerated below;
 A set of rules each consisting of a left side that determines the
applicability of the rule and a right side that describes the
operation to be performed if the rule is applied.
 A database of current facts established during the process of
inference.
 A control strategy that specifies the order in which the rules
will be compared with facts in the database and also specifies
how to resolve conflicts in selection of several rules or selection of
more facts.
 A rule firing module.

Production systems
4
• The production rules operate on the
knowledge database.
• Each rule has a precondition—that is,
either satisfied or not by the
knowledge database.
• If the precondition is satisfied, the
rule can be applied.
• Application of the rule changes the
knowledge database.
• The control system chooses which
applicable rule should be applied and
ceases computation
termination condition
when a
on the
knowledge database is satisfied.

Example: Water Jug Problem
5
You are given two jugs, a 4-gallon one and a 3-gallon one, a pump which has unlimited water which
you can use to fill the jug, and the ground on which water may be poured. Neither jug has any
measuring markings on it. How can you get exactly 2 gallons of water in the 4-gallon jug?
• Solution:

Production Rules for water jug problem
6
Rule No. Goal comment Condition Initial condition
R1 Fill 4-gal jug (x,y) if x < 4 → (4,y)
R2 Fill 3-gal jug (x,y) if y < 3 → (x,3)
R3 Empty 4-gal jug on ground (x,y) if x > 0 → (0,y)
R4 Empty 3-gal jug on ground (x,y) if y > 0 → (x,0)
R5 Pour water from 3-gal jug to 4-gal jug (x,y) if x+y >=4 and y > 0 → (4, y - (4 - x))
R6 Pour water from 4-gal jug to 3-gal jug (x,y) if x+y >=3 and x > 0 → (x - (3-y), 3)
R7 Pour all of water from 3-gal jug into 4-gal jug (x,y) if x+y <=4 and y >0 → (x+y, 0)
R8 Pour all of water from 4-gal jug into 3-gal jug (x,y) if x+y <=3 and x >0 → (0, x+y)
State Representation and Initial State : We will represent a state of the problem as a tuple (x, y).
Where, x represents the amount of water in the 4-gallon jug and
y represents the amount of water in the 3-gallon jug.
Note: 0 ≤x≤ 4, and 0 ≤y ≤3.
Our initial state: (0, 0)
Goal Predicate state: = (2, y) where 0≤ y≤ 3.
Operators: we must define a set of operators that will take us from one state to another;

Characteristics of a Production System
7
• There are mainly four characteristics of the
production system in AI that is;
Simplicity,
Modifiability,
Modularity, and
Knowledge-intensive.

8
1. Simplicity:
• The production rule in AI is in the form of an ‘IF-THEN’ statement.
• Every rule in the production system has a unique structure.
• It helps represent knowledge and reasoning in the simplest way possible to solve real-
world problems.
• Also, it helps improve the readability and understanding of the production rules.
2. Modularity:
• The modularity of a production rule helps in its incremental improvement as the
production rule can be in discrete parts.
• The production rule is made from a collection of information and facts that may not
have dependencies unless there is a rule connecting them together.
• The addition or deletion of single information will not have a major effect on the
output. Modularity helps enhance the performance of the production system by
adjusting the parameters of the rules.

9
3. Modifiability:
• The feature of modifiability helps alter the rules as per requirements.
• Initially, the skeletal form of the production system is created.
• We then gather the requirements and make changes in the raw structure of the
production system.
• This helps in the iterative improvement of the production system.
4. Knowledge-intensive:
• Production systems contain knowledge in the form of a human spoken language, i.e.,
English.
• It is not built using any programming languages. The knowledge is represented in
plain English sentences.
• Production rules help make productive conclusions from these sentences.

Problem Characteristics
10
• Is the problem decomposable into set of independent smaller or easier sub problems?
• Can the solution step be ignored or at least undone if they prove unwise?
• Is the problem universally predictable?
• Is a good solution to the problem obvious without comparison to all the other possible
solutions?
• Is the desire solution a state of world or a path to a state?
• Is a large amount of knowledge absolutely required to solve the problem?
• Will the solution of the problem required interaction between the computer and the
person?

Types of production systems
11
• There are four types of production systems that help in categorizing
methodologies for solving different varieties of problems;
1. Monotonic Production System:
In this type of a production system, the rules can be applied simultaneously as the use of
one rule does not prevent the involvement of another rule that is selected at the same time.
2. Partially Commutative Production System:
This class helps create a production system that can give the results even by interchanging
the states of rules.
If using a set of rules transforms State A into State B, then multiple combinations of those
rules will be capable to convert State A into State B.

12
3. Non-monotonic Production System:
This type of a production system increases efficiency in solving problems.
The implementation of these systems does not require backtracking to correct the previous
incorrect moves.
The non-monotonic production systems are necessary from the implementation point of
view to find an efficient solution.
4. Commutative System:
Commutative systems are helpful where the order of an operation is not important. Also,
problems where the changes are reversible use commutative systems.
On the other hand, partially commutative production systems help in working on problems,
where the changes are irreversible such as a chemical process.
When dealing with partially commutative systems, the order of processes is important to
get the correct results.

What is Artificial Intelligence?
• Artificial Intelligence is concerned with the design of intelligence in an artificial device.
John McCarthy, 1956.
• The exciting new effort to make computers think … machines with minds, in the full
literal sense. Haugeland, 1985
• The study of mental faculties through the use of computational models.
Charniak and McDermott, 1985
• A field of study that seeks to explain and emulate intelligent behavior in terms of
computational processes. Schalkoff, 1990
• The study of how to make computers do things at which, at the moment, people are better.
Rich & Knight, 1991
13

“(automation of) activities that we associate
with human thinking, activities such as
decision making, problem solving,
learning...” [Bellman 1978]
“The study of mental faculties through the
use of computational models...” [Charniak
and McDertmott 1985]
“The study of how to make computers do
things at which, at the moment, people are
better...” [Rich and Knight 1991]
“The branch of computer science that is
concerned with the automation of intelligent
behavior...” [Luger and Stubblefield 1993]
14
human-like vs rational
thought
vs
behavior
Systems that think like humans Systems that think rationally
Systems that act like humans System that act rationally

Various approaches to AI
• So, basically four approaches have been followed.
• As one might expect, a tension exists between approaches centered on humans
and approaches centered around rationality.
A human centered approach must be an empirical science, involving
hypothesis and experimental confirmation.
A rationalist approach involves a
engineering.
combination of mathematics and
15

Acting humanly: The Turing Test approach
16
• The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory
operational definition of intelligence.
• Turing defined intelligent behavior as the ability to achieve human-level performance
in all cognitive tasks, sufficient to fool an interrogator.
• The test he proposed is that the computer should be interrogated by a human via a
teletype, and passes the test if the interrogator cannot tell if there is a computer or a
human at the other end.
“If the human cannot tell whether the
responses from other side of the wall are
coming from a human or computer, then
the computer is intelligent.”

Thinking humanly
17
• If we are going to say that a given program thinks like a human, we must have some way
of determining how humans think.
• We need to get inside the actual workings of human minds.
• Thinking like humans is important in Cognitive Science applications like intelligent
tutoring and speech recognition.
Thinking rationally
• The Greek philosopher Aristotle was one of the first to attempt to codify "right thinking,"
that is, irrefutable reasoning processes.
• His famous syllogisms provided patterns for argument structures that always gave
correct conclusions given correct premises.
Acting rationally
• Acting rationally means acting so as to achieve one's goals, given one's beliefs.
• An agent is just something that perceives and acts.
• In this approach, AI is viewed as the study and construction of rational agents.

Intelligent Agents
• “An agent is anything that can be viewed as perceiving its environment through sensors
and acting upon that environment through actuators.”
• For example;
A human agent has eyes, ears and other organs for sensors and hands, legs, vocal
tract and so on for actuators.
A robotic agent might have cameras and infrared range finders for sensors and
various motors for actuators.
A software agent receives keystrokes, file contents and network packets as sensory
inputs and acts on the environment by displaying on the screen, writing files, and
sending network packets.
18

Agents and Environments
• Agents interact with environments through
sensors and actuators.
• The term percept is used to refer to the agent’s
perceptual inputs at any given instant.
• An agent’s percept sequence is the complete
history of everything the agent has ever
perceived.
• Also, we can say that, an agent’s behavior is
described by the agent function that maps any
given percept sequence to an action.
• Internally, the agent function for an artificial
agent will be implemented by an agent program.
19

Example: Vacuum Cleaner
• Consider the simple example of, Vacuum-
cleaner world.
• This particular world has just two locations:
squares A and B.
• The vacuum agent perceives which square it is
in and whether there is dirt in the square.
• It can choose to move left, move right, absorb
the dirt or do nothing.
• One very simple agent function is; if the
current square is dirty then absorb the dirt
otherwise move to the other square.
20
Percept Sequence Action
[A, Clean] Right
[A, Dirty] Absorb
[B, Clean] Left
[B, Dirty] Absorb
[A, Clean], [A, Clean] Right
[A, Clean], [A, Dirty] Absorb
:
:
:
:
[A, Clean], [A, Clean], [A, Clean] Right
[A, Clean], [A, Clean], [A, Dirty] Absorb
:
:
:
:

Example: Vacuum Cleaner
21

Concept of rationality
• A rational agent is one that does the right thing-conceptually speaking, every entry in the
table for the agent function is filled out correctly.
• What is rational at any given time depends on four things;
The performance measure that defines the criterion of success.
The agent’s prior knowledge of the environment.
The actions that the agent can perform.
The agent’s percept sequence to date.
• This leads to a definition of a rational agent;
“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”.
22

Nature of Environments
• As we had to specify the performance measure, the environment, and the agent’s actuators
and sensors. We group all these under the heading of the task environments.
• This is also called as PEAS(Performance, Environment, Actuators, Sensors).
• Let us consider a complex problem; An automated taxi driver.
23
Agent Type Performance
Measure
Environment Actuators Sensors
Taxi Driver Safe, fast, legal,
comfortable trip,
maximize profits
Roads, other traffic,
pedestrians,
customers
Steering, accelerator,
brake, signal, horn,
display
Cameras, sonar,
speedometer, GPS,
odometer,
accelerometer, engine
sensors, keyboard

Examples of Agent types and their PEAS descriptions
Agent Type Performance
Measure
Environment Actuators Sensors
Medical
diagnosis
system
Healthy patient,
reduced costs
Patient, hospital,
staff
Display of questions,
tests, diagnosis,
treatments, referrals
Keyboard entry of
symptoms, findings,
patient’s answers
Satellite image
analysis
system
Correct image
categorization
Downlink from
orbiting satellite
Display of scene
categorization
Color pixel arrays
Part-picking
robot
Percentage of parts
in correct bins
Conveyor belt with
parts, bins
Jointed arm and
hand
Camera, joint angle
sensors
Refinery
controller
Purity, yield, safety Refinery operators Valves, pumps,
heaters, displays
Temperature, pressure,
chemical sensors
Interactive
English tutor
Student’s score on
test
Set of students,
testing agency
Display of exercises,
suggestions,
corrections
Keyboard entry

Properties of task environments
1. Fully observable vs partially observable:
 If an agent’s sensors give it access to the complete state of the environment at each point in
time, then we say that the task environment is fully observable.
 An environment might be partially observable because of noisy and inaccurate sensors or
because parts of the state are simply missing from the sensor data.
 For example, a vacuum agent with only a local dirt sensor cannot tell whether there is dirt in
other squares and an automated taxi cannot see what other drivers are thinking.
 If the agent has no sensors at all then the environment is unobservable.
2. Single agent vs Multiagent:
 The distinction between single-agent and multiagent environments may seem simple enough.
 For example, an agent solving a crossword puzzle by itself is clearly in a single-agent
environment.
 Whereas, an agent playing chess is in a two-agent environment.
25

3. Deterministic vs Stochastic:
 If the next state of the environment is completely determined by the current state and the
action executed by the agent, then we say the environment is deterministic, otherwise, it is
stochastic.
 Most real situations are so complex that it is impossible to keep track of all the unobserved
aspects, for practical purposes, they must be treated as stochastic.
 Taxi driving is stochastic in this sense, because one can never predict the behaviour of traffic
exactly. The vacuum world is deterministic.
4. Episodic vs Sequential:
 In an episodic task environment, the agent’s experience is divided into atomic episodes.
 In each episode the agent receives a percept and then performs a single action.
 For example, an agent that has to spot defective parts on an assembly line. Here, the
current decision doesn’t affect whether the next part is defective or not.
 In sequential environments, the current decision could affect all future decisions. (eg: Chess
and taxi driving)
26

5. Static vs Dynamic:
 If the environment can change while an agent is deliberating, then we say the
environment is dynamic for that agent otherwise, it is static.
 If environment does not change with passing time but agent’s performance score does,
then we say the environment is semi-dynamic.
 Example of dynamic task environment is taxi driving, as the other cars and taxi itself
keep moving while the driving algorithm hesitates about what to do next.
 Static environments are easy to deal with because the agent need not keep looking at the
world while it is deciding on an action. Crossword puzzles are static.
6. Discrete vs Continuous:
 The discrete or continuous distinction applies to the state of the environment, to the way
time is handled, and to the percepts and actions of the agent.
 For eg: chess environment has a finite number of distinct states, set of percepts and
actions. But, taxi driving is a continuous state and time problem.
27

7. Known vs Unknown: (not a strict property of environment)
 In a known environment, the outcomes for all actions are given. And if the
environment is unknown, the agent will have to learn how it works in order to make
good decisions.
 A solitaire card game is an example of known environment where we know the
rules but still unable to see the cards that have not been turned over.
 For unknown environment, consider a new video game, where the screen may
show the entire game state but still we don’t know what the buttons do until we try
them.
28

Examples
Task environment Fully
observable/
Partially
observable
Single/ Multi-
agent
Deterministic/
Stochastic
Episodic/
Sequential
Static/
Dynamic
Discrete/
Continuous
1.Cross word puzzle Fully Single Deterministic Sequential Static Discrete
2.Chess with a clock Fully Multi Deterministic Sequential Semi-
Dynamic
Discrete
3. Poker Partially Multi Stochastic Sequential Static Discrete
4. Taxi driving Partially Multi Stochastic Sequential Dynamic Continuous
5. Medical
diagnosis
Partially Single Stochastic Sequential Dynamic Continuous
6. Image analysis Fully Single Deterministic Episodic Semi-
Dynamic
Continuous
7. Part picking
robot
Partially Single Stochastic Episodic Dynamic Continuous
8. Interactive
English Tutor
Partially Multi Stochastic Sequential Dynamic Discrete

The Structure of Agents
• The job of AI is to design an agent program that implements the agent function(the mapping from
percepts to actions). We assume this program will run on some sort of computing device with
physical sensors and actuators----we call this the architecture.
 Agent = architecture + program.
• The agent program takes just the current percept as input because nothing more is available from
the environment.
• We can describe the agent programs in the simple pseudocode language that is defined as follows;
30

The Structure of Agents
• The table-driven approach to agent construction certainly fails.
• To understand the reason, consider the example of automated taxi, the visual input from a single
camera comes in at rate of roughly 27 MB/sec(ie. 30 frames/sec, 640*480 pixels with 24 bits of
color information). This gives a lookup table with over 10250,000,000,000 entries for an hours driving.
• Even the lookup table for chess would have at least 10150 entries.
• So, no physical agent in this universe will have the space to store the table, no agent could ever
learn all table entries right from its experience.
• We can outline four basic kinds of agent programs that embody the principles underlying almost
all intelligent systems;
 Simple reflex agents,
 Model-based reflex agents,
 Goal-based agents and
 Utility-based agents.
31

Simple reflex agents
• The simplest kind of agent is the simple reflex agent. These agents select actions on the basis of
the current percept, ignoring the rest of the percept history. For example, the vacuum agent.
• Notice that the vacuum agent program is very small compared to the corresponding table. The most
obvious reduction comes from ignoring the percept history, which cutsdown the number of
possibilities from 4T to just 4. A further, small reduction comes from the fact that when the current
square is dirty, the action does not depend on the location.
32
Absorb

• Simple reflex behaviors occur even in more complex environments. Imagine yourself as
the driver of the automated taxi.
• If the car in front brakes and its brake lights become on, then we should notice this and
initiate braking. In other words, some processing is done on the visual input to
establish the condition we call “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-braking then initiate-braking.
33

• A more general and flexible approach is
first to build a general-purpose
interpreter for condition–action
rules and then to create rule sets for
specific task environments.
• We use rectangles to denote the
current
agent’s
ovals
internal state of the
decision process, and
to represent the
background information used in
the process.
• The INTERPRET-INPUT function
generates an abstracted description of
the current state from the percept, and
the RULE-MATCH function returns the
first rule in the set of rules that
matches the given state description.
34

Model-based reflex agents
• The most effective way to handle partial observability is for the agent to keep track of the part of
the world it can’t see now.
• That is, the agent should maintain some sort of internal state that depends on the percept
history and thereby reflects at least some of the unobserved aspects of the current state.
• Updating this internal state information as time goes by requires two kinds of knowledge to be
encoded in the agent program;
 First, we need some information about how the world evolves independently of the agent—for
example, that an overtaking car generally will be closer behind than it was a moment
ago.
 Second, we need some information about how the agent’s own actions affect the world—for
example, that when the agent turns the steering wheel clockwise, the car turns to the
right.
• This knowledge about “how the world works”—whether implemented in simple Boolean circuits
or in complete scientific theories—is called a model of the world. An agent that uses such a model
is called a model-based agent.
35

• Figure gives the structure of the model-based reflex agent with internal state, showing how
the current percept is combined with the old internal state to generate the updated
description of the current state, based on the agent’s model of how the world works.
36

• The agent program is shown in Figure.
The interesting part is the function
UPDATE-STATE, which is responsible
for creating the new internal state
description.
• The details of how models and states are
represented vary widely depending on
the type of environment and the
particular technology used in the agent
design.
• Regardless of the kind of representation
used, it is seldom possible for the agent
to determine the current state of a
partially observable environment exactly.
• Instead, the box labelled “what the world
is like now” represents the agent’s “best
guess”.
37

Goal-based agents
• Knowing something 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, turn right, or go straight
on.
• The correct decision depends on where
the taxi is trying to get to.
• In other words, the agent needs some
sort of goal information that describes
situations that are desirable.
• The agent program can combine this
with the model to choose actions that
achieve the goal.
38

Goal-based agents
• Sometimes goal-based action selection is straightforward—for example, when goal
satisfaction results immediately from a single action. Sometimes it will be trickier—for
example, when the agent has to consider long sequences of twists and turns in order to find
a way to achieve the goal.
• Although the goal-based agent appears less efficient, it is more flexible because the
knowledge that supports its decisions is represented explicitly and can be modified.
• If it starts to rain, the agent can update its knowledge of how effectively its brakes will
operate; this will automatically cause all of the relevant behaviors to be altered to suit the
new conditions.
• The goal-based agent’s behavior can easily be changed to go to a different destination,
simply by specifying that destination as the goal.
• The reflex agent’s rules for when to turn and when to go straight will work only for a single
destination; they must all be replaced to go somewhere new.
39

Utility-based agents
• The term utility is used as a general performance measure that allows a comparison of
different world states according to exactly how happy they would make the agent.
• The performance measure assigns a score to any given sequence of environment states, so it
can easily distinguish between more and less desirable ways of getting to the taxi’s
destination.
• An agent’s utility function is essentially an internalization of the performance measure.
• If the internal utility function and the external performance measure are in agreement, then
an agent that chooses actions to maximize its utility will be rational according to the
external performance measure.
• Technically speaking, a rational utility-based agent chooses the action that maximizes the
expected utility of the action outcomes—that is, the utility the agent expects to derive, on
average, given the probabilities and utilities of each outcome.
40

Utility-based agents
41

Learning agents
• A learning agent can be divided into four conceptual components;
Learning element: which is responsible for making improvements. It 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.
Performance element: which is responsible for selecting external actions, it takes in
percepts and decides on actions.
Critic: tells the learning element how well the agent is doing with respect to a fixed
performance standard.
Problem generator: It is responsible for suggesting actions that will lead to new and
informative experiences.
42

Learning agents
43

Structure of different agents 44
Simple reflex
agent
Goal based agent
Learning agents
Model based
agent
Utility based agent

References
45
• “Artificial intelligence: A modern approach”, S. J. Russell and P. Norvig,
Pearson, 3rd Edition
• “Artificial Intelligence”, Elaine R. and Kevin K., McGraw Hill, 2nd edition
Sanjivani College of Engineering, Kopargaon Dept of Information Technology Miss K. S. Ubale
Dept of Information Technology

Thank you
Sanjivani College of Engineering, Kopargaon Dept of Information Technology K. S. Ubale
Dept of Information Technology

Unit 1 Fundamentals of Artificial Intelligence-Part II.pptx

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