This document discusses weak slot-and-filler knowledge representation structures. It describes how slots represent attributes and fillers represent values. Semantic networks are provided as an example where nodes represent objects/values and links represent relationships. Property inheritance allows subclasses to inherit attributes from more general superclasses. Frames are also discussed as a type of weak structure where each frame contains slots and associated values describing an entity. The document notes challenges with tangled hierarchies and provides examples of how to resolve conflicts through inferential distance in the property inheritance algorithm.
Search techniques in ai, Uninformed : namely Breadth First Search and Depth First Search, Informed Search strategies : A*, Best first Search and Constraint Satisfaction Problem: criptarithmatic
Search techniques in ai, Uninformed : namely Breadth First Search and Depth First Search, Informed Search strategies : A*, Best first Search and Constraint Satisfaction Problem: criptarithmatic
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Weak Slot and Filler Structures
Representation in a Semantic Net
Frames can also be regarded as an extension to Semantic nets. Indeed it is not clear where the distinction between a semantic net and a frame ends. Semantic nets initially we used to represent labelled connections between objects. As tasks became more complex the representation needs to be more structured. The more structured the system it becomes more beneficial to use frames. A frame is a collection of attributes or slots and associated values that describe some real world entity. Frames on their own are not particularly helpful but frame systems are a powerful way of encoding information to support reasoning. Set theory provides a good basis for understanding frame systems. Each frame represents:
a class (set), or
an instance (an element of a class).
Frame Knowledge Representation
We have already met this type of structure when discussing inheritance in the last lecture. We will now study this in more detail.
I. Hill climbing algorithm II. Steepest hill climbing algorithmvikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Weak Slot and Filler Structures
Representation in a Semantic Net
Frames can also be regarded as an extension to Semantic nets. Indeed it is not clear where the distinction between a semantic net and a frame ends. Semantic nets initially we used to represent labelled connections between objects. As tasks became more complex the representation needs to be more structured. The more structured the system it becomes more beneficial to use frames. A frame is a collection of attributes or slots and associated values that describe some real world entity. Frames on their own are not particularly helpful but frame systems are a powerful way of encoding information to support reasoning. Set theory provides a good basis for understanding frame systems. Each frame represents:
a class (set), or
an instance (an element of a class).
Frame Knowledge Representation
We have already met this type of structure when discussing inheritance in the last lecture. We will now study this in more detail.
I. Hill climbing algorithm II. Steepest hill climbing algorithmvikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
2. Inheritable knowledge
• The relational knowledge base determines a set of
attributes and associated values that together
describe the objects of knowledge base.
E.g. Player_info(“john”,”6.1”,180,right_throws)
• The knowledge about the objects, their attributes and
their values need not be as simple as shown.
• One of the most powerful form of inference
mechanisms is property inheritance.
Player Height Weight Bats_throws
John 6.1 180 Right_throws
Sam 5.10 170 right_right
Jack 6.2 215 Bats_throws
3. • Property Inheritance
• Here elements of specific classes inherit attributes
and values from more general classes in which
they are included.
• In order to support property inheritance objects
must be organized into classes and classes must be
arranged in generalization hierarchy.
4. Here,
Lines ==attributes and boxed nodes== object/values of attributes of an object.
This structure is also called as slot and filler structure. These structures are the
devices to support property inheritance along isa and instance links.
Mammal
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
colour team
person(Owen) instance(Owen, Person)
team(Owen, Liverpool)
Here,
Lines ==attributes and boxed nodes== object/values of attributes of an object.
This structure is also called as slot and filler structure. These structures are the
devices to support property inheritance along isa and instance links.
Mammal
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
colour team
5. • Advantage of slot and filler structures:
1. monotonic reasoning can be performed more
effectively than with pure logic and non monotonic
reasoning is easily supported.
2. Makes it easy to describe properties of relations.
e.g. “does Owen has-part called nose?”
3. Form of object oriented programming and has
advantages such as modularity and ease of viewing
by people.
6. Slot and filler structures
Attribute= slot and its value= filler
Weak slot and filler structure Strong slot and filler structure
FramesSemantic nets ScriptsConceptual Dependency
Weak slot and filler structures: are “Knowledge- Poor” or
“weak” as very little importance is given to the specific
knowledge the structure should contain.
7. Semantic nets
• In semantic nets information is represented as:
– set of nodes connected to each other by a set of
labelled arcs.
• Nodes represent: various objects / values of the
attributes of object .
• Arcs represent: relationships among nodes.
Mammal
Person
Jack
Nose
Blue Chicago Royals
isa
instance
has-part
uniform
color team
8. • In this network we could use inheritance to derive
the additional info:
has_part(jack, nose)
Intersection Search
One way to find relationships among objects is to spread
the activation(links) out from two nodes and find out
where it meets
Ex: relation between :
Red and liverpool
Mammal
Person
Owen
Nose
Red Liverpool
isa
instance
has-part
uniform
color team
9. • Representing non binary predicates:
1. Unary –
e.x. Man(marcus) can be converted into:
instance(marcus,Man)
2. Other arities-
e.x. Score(india,australia,4-1)
3 or more place predicates can be converted to binary
form as follows:
1. Create new object representing the entire
predicate.
2. Introduce binary predicates to describe relation to
this new object.
11. Ex. 2. “john gave the book to Mary”
give(john,mary,book)
EV 1
instance
Give
John
Mary
Book
BK1
instance
Objectagent
beneficiary
12. Making some important distinctions
1. “john has height 72”
2. “john is taller than Bill”
John 72
height
John Bill
H1 H2
height height
72
Value
greater_than
13. Partitioned semantic nets
• Used to represent quantified expressions in
semantic nets.
• One way to do this is to partition the semantic net
into a hierarchical set of spaces each of which
corresponds to the scope of one or more variable.
• “the dog bit the mail carrier” [partitioning not required]
d
Dogs
b
Bite
m
Mail-Carrier
isa isa isa
assailant victim
14. • “every dog has bitten a mail carrier”
x: dog (x) y: mail-carrier(y) bite(x, y)
• How to represent universal quantifiers?
– Let node ‘g’ stands for assertion given above
– This node is an instance of a special class ‘GS’ of
general statements about the world.
– Every element in ‘GS’ has 2 attributes:-
• Form - states relation that is being asserted.
• connections - one or more, one for each of the universally
quantified variables.
– ‘SA’ is the space of partitioned sementic net.
15. • “every dog has bitten a mail carrier”
SA
S1
d
Dogs
b
Bite
m
Mail-Carrier
isa isa isa
assailant victim
g
GS
isa
form
16. • “Every dog in the town has bitten the constable”
SA
m
Constables
isa
S1
d
Dogs
b
Bite
isa isa
assailant
g
GS
isa
form
victim
17. • “Every dog in the town has bitten every constable”
SAConstables
S1
d
Dogs
b
Bite
isa isa
assailant
gGS
isa
form
victim
c
isa
18. • More examples of sementic nets:
• “ Mary gave the green flowered vase to her
favourite cousin”
EV 1
instance
Give
Mary
cousin
vase
Objectagent
beneficiary
Colour_pattern
Green
flowered
favourite
19. • “every batsman hits a ball”
SA
S1
b
Batsman Hits
b
Balls
isa isa isa
action Acts_on
g
GS
isa
form
h
20. • “Tweety is a kind of bird who can fly. It is Yellow
in colour and has wings.”
Bird
Tweety
Wings
instance
has-part
yellow
fly
colour
action
21. • Represent following using sementic nets:-
Tom is a cat. Tom caught a bird. Tom is owned by John. Tom is
ginger in color. Cats like cream.The cat sat on the mat. Acat is
a mammal. Abird is an animal. All mammals are
animals.mammals have fur.
22. Frames
• Another kind of week slot and filler structure.
• Frame is a collection of attributes called as slots
and associated values that describe some entity in
the world (filler).
• Consider,
Room
Hotel room
isa
Hotel bed
contains
Hotel Chair
contains
Location
Chair
Sitting_on
4
20-40 cms
isa
use
legs
height
Room No 2
instance
23. Hotel Room
isa : Room
contains: Hotel Bed
contains: Hotel Chair
Hotel Chair
isa: Chair
use: sitting_on
location: Hotel Room
. . . . . . . . . .
. . . . . . . . . .
. . . . . . . . . .
Frame System for Hotel Room
Frame structure for Hotel Room
Frame structure for Hotel Chair
Frame structure for all remaining
attributes
24. Person Jack_Roberts
isa: Mammal instance: Fielder
cardinality: 6,000,000,000 height: 5-10
* Handed: right balls: right
batting_avg: 0.309
Adult__Male team: Chicago cubs
isa: Person uniform_color: blue
Cardinality: 2,000,000,000 Fielder
* Height: 5- 10 isa: ML_Baseball_Player
cardinality: 376
ML_Baseball_Player batting_avg: 0.262
isa: Adult_Male
cardinality: 624 ML_Baseball_Team
* height: 6-1 isa: Team
* bats: equal to handed cardinality: 26
* batting-avg: 0.252 team_size: 24
* team: manager:
*uniform_color:
Individual
frame
25. • Meta Class: special class whose elements themselves are classes.
– If X is meta class and Y is another class which is an element of X, then Y inherits
all the attributes of X.
• Other ways of relating classes to each other
1. Mutually disjoint: 2 classes are mutually disjoint if they are
guaranteed to have no elements in common.
2. Is covered by: relationship is called as ‘covered-by’ when we have
a class and it has set of subclasses, the union of which is equal to the
superclass.
ML_Baseball_Player
isa
Fielder Pitcher
isa
Catcher
isa
American
Leaguer
isa
National
Leaguer
isa
Jack
instanceinstance
27. Tangled Hierarchies
• Hierarchies that are not trees
• Usually hierarchy is an arbitrary directed acyclic
graph.
• Tangled hierarchies requires new property
inheritance algorithm.
28. •
FIGURE A
• Can fifi fly?
• The correct answer must be ‘no’.
– Although birds in general can fly, the subset of birds , ostriches does not.
– Although class pet bird provides path from fifi to bird and thus to the answer that fifi
can fly, it provides no info that conflicts with the special case knowledge associated with
class ostrich, so it should hove no effect on the answer.
isa
isaisa
isa
Ostrich
fly :no
fifi
fly : ?
Bird
fly :yes
Pet-Bird
29. FIGURE B
• Is Jack Pacifist?
– Ambiguity
• One way to solve ambiguity is to base the new inheritance algo based on path
length:
– Using BFS start with the frame for which slot value is needed.
– Follow its instance links, then follow isa links upwards .
– If the path produces a value it can be terminated, as can all other paths once their length
exceeds that of the successful path.
instanceinstance Jack
Pacifist : ?
Quaker
pacifist: true
Republican
pacifist: False
30. • Using this technique our answers to fifi problem is :’no’ and
for jack problem we get 2 values hence ‘contradiction’.
• Now consider following hierarchies:
FIGURE C
• Our new algo gives answer: fifi can fly. i.e. Fly: yes.
isa
instance
instance
isa
White-Plumed
Ostrich
fifi
fly : ?
Bird
fly :yes
Pet-Bird
Plumed Ostrich
isa
Ostrich
fly: no
isa
31. FIGURE D
• Here our new algo reaches Quaker and
deduces pacifist:true and stops without
noticing further contradiction.
instanceinstance
Jack
Pacifist : ?
Quaker
pacifist: true
Conservative
Republican
isa
Republican
pacifist: false
32. • Solution to the problem is to base our algo not
based on path length but on inferential distance.
• Class 1 is closer to class2 than class 3 if class1 has an
inferential path through class2 to class3.
• For figure A answer is no
• For figure B Contradiction
PLEASE REFER PROPERTY INHERITANCE ALGO
Rich and Knight pg. 204-205 3rd edition.
Class 2Class 1 Class 3