Semantic nets were originally proposed in the 1960s as a way to represent the meaning of English words using nodes, links, and link labels. Nodes represent concepts, objects, or situations, links express relationships between nodes, and link labels specify particular relations. Semantic nets can represent data through examples, perform intersection searches to find relationships between objects, partition networks to distinguish individual from general statements, and represent non-binary predicates. While semantic nets provide a visual way to organize knowledge, they can have issues with inheritance and placing facts appropriately.
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
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
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
Knowledge representation and Predicate logicAmey Kerkar
This presentation is specifically designed for the in depth coverage of predicate logic and the inference mechanism :resolution algorithm.
feel free to write to me at : amecop47@gmail.com
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
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
THIS DESCRIBES VARIOUS ELEMENTS OF TRANSPORT PROTOCOL IN TRANSPORT LAYER OF COMPUTER NETWORKS
THERE ARE SIX ELEMENTS OF TRANSPORT PROTOCOL NAMELY
1. ADDRESSING
2. CONNECTION ESTABLISHMENT
3.CONNECTION REFUSE
4.FLOW CONTROL AND BUFFERS
5.MULTIPLEXING
6.CRASH RECOVERY
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
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.”
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Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
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
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
THIS DESCRIBES VARIOUS ELEMENTS OF TRANSPORT PROTOCOL IN TRANSPORT LAYER OF COMPUTER NETWORKS
THERE ARE SIX ELEMENTS OF TRANSPORT PROTOCOL NAMELY
1. ADDRESSING
2. CONNECTION ESTABLISHMENT
3.CONNECTION REFUSE
4.FLOW CONTROL AND BUFFERS
5.MULTIPLEXING
6.CRASH RECOVERY
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
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
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
Passmore, D. L., & Baker, R. M. (2016). Social networks of performance. Session presented at The Performance Improvement Conference of the International Society for Performance Improvement, Philadelphia, Pennsylvania.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
(WIP) Neural Concept Network is a directed network for representation, searching, analyzing, learning, and forming concepts currently under development.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
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2. Meaning of semantic nets
• Semantic nets were originally proposed in the
early 1960s by M. Ross Quillian to represent
the meaning of English words
• The basic idea behind semantic nets is that
how it carries meaning of the concept and how
is related with other concepts
• Semantic nets consist of nodes, links (edges)
and link labels.
3. ELEMENTS USEDIN SEMANTIC NETS
• In the semantic network diagram, nodes appear as
circles or ellipses or rectangles to represent objects
such as physical objects, concepts or situations.
• Links or Arcs appear as arrows to express the
relationships between objects
• Link Labels specify particular relations.
• Relationships provide the basic structure for organizing
knowledge.
• As nodes are associated with other nodes semantic
nets are also referred to as associative nets.
4. Example of semantic network
IS A
SUBSET OF SUBSET OF
MEMBER OF MEMBER OFSISTER OF
6. Example of semantic network
Is intended to represent the data:
• 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.
• A cat is a mammal.
• A bird is an animal.
• All mammals are animals.
• Mammals have fur.
7. INTERSECTION SEARCH
In semantic nets, to find
relationships among objects are
determined by spreading activation
out from each of 2 nodes and
identify where the activation
meets. This process is called
intersection search.
9. Representingnon-binary predicates
• Semantic nets are a natural way to represent
relationships that would appear as ground
instances of binary predicates in logic
• For example :
• Is a(baseball player, pitcher)
• Is a(baseball player, fielder)
• Instance(three-finger brown ,pitcher)
• Instance(pee-wee Reese ,fielder)
• Team(three-finger brown , Chicago cubs)
• Team(pee-wee Reese ,Brooklyn dodgers)
11. Making some important distinctions
• By defining the relationship the complexity of
the relation can also be easily represented in
semantic nets .
• For example : tom weight is 60 kg.
13. PARTITIONEDSEMANTICNETS
Hendrix developed the partitioned semantic network
to represent the difference between the description of
an individual object or process and the description of a
set of objects. The set description involves
quantification.
Hendrix partitioned a semantic network whereby a
semantic network, loosely speaking, can be divided
into one or more networks for the description of an
individual.
The central idea of partitioning is to allow groups,
nodes and arcs to be bundled together into units called
spaces – fundamental entities in partitioned networks,
on the same level as nodes and arcs
14. PARTITIONEDSEMANTICNETS
• Suppose that we wish to make a specific
statement about a dog, Danny, who has
bitten a postman, Peter:
– " Danny the dog bit Peter the postman"
• Hendrix’s Partitioned network would express
this statement as an ordinary semantic
network:
17. PARTITIONEDSEMANTICNETS
• "Every dog in town has bitten the postman“
General
Statement town dog
D
bite
B
postman
P
is_a is_a is_a
assailant victim
S1
G
form
SA
is_a
dog
18. "John believes that pizza is tasty"
John
believes
event
pizza tasty
object property
agent
is_a
object
has
is_a is_a
space
19. "Every student loves to party"
GS1
General
Statement
student party love
p1 l1
agent
is_a
is_a
receiver
is_a is_aS2
GS2
s1
S1
is_a
form
exists
form
20. advantages
• Easy to visualise and understand.
• The knowledge engineer can arbitrarily
defined the relationships.
• Related knowledge is easily
categorised.
• Efficient in space requirements.
• Related knowledge is
easily clustered.
21. disadvantages
• Inheritance (particularly from
multiple sources and when
exceptions in inheritance are
wanted) can cause problems.
• Facts placed inappropriately
cause problems.
• No standards about node and arc values
• This not describes the attributes.
