1) This document discusses semantic networks, which are a knowledge representation technique used in artificial intelligence. Semantic networks represent knowledge through nodes and links, where nodes represent concepts or objects, and links represent relationships between the nodes.
2) As an example, a simple semantic network is presented representing facts about a cat named Jerry - that Jerry is a cat, a mammal, owned by Jay, white in color, and likes cheese.
3) The document outlines different types of semantic networks including definitional, assertional, implicational, and learning networks. It also discusses advantages such as being a natural representation of knowledge, and disadvantages including lack of quantifiers and lack of intelligence.
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
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 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.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
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.
Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
AN GROUP BEHAVIOR MOBILITY MODEL FOR OPPORTUNISTIC NETWORKS csandit
Mobility is regarded as a network transport mechanism for distributing data in many networks.
However, many mobility models ignore the fact that peer nodes often carried by people and
thus move in group pattern according to some kind of social relation. In this paper, we propose
one mobility model based on group behavior character which derives from real movement
scenario in daily life. This paper also gives the character analysis of this mobility model and
compares with the classic Random Waypoint Mobility model.
An information-theoretic, all-scales approach to comparing networksJim Bagrow
My presentation at NetSci 2018 on Portrait Divergence, a new approach to comparing networks that is simple, general-purpose, and easy to interpret.
The preprint: https://arxiv.org/abs/1804.03665
The code: https://github.com/bagrow/portrait-divergence
MODELING SOCIAL GAUSS-MARKOV MOBILITY FOR OPPORTUNISTIC NETWORK csandit
Mobility is attracting more and more interests due to its importance for data forwarding
mechanisms in many networks such as mobile opportunistic network. In everyday life mobile
nodes are often carried by human. Thus, mobile nodes’ mobility pattern is inevitable affected by
human social character. This paper presents a novel mobility model (HNGM) which combines
social character and Gauss-Markov process together. The performance analysis on this
mobility model is given and one famous and widely used mobility model (RWP) is chosen to
make comparison..
6. kr paper journal nov 11, 2017 (edit a)IAESIJEECS
Knowledge Representation (KR) is a fascinating field across several areas of cognitive science and computer science. It is very hard to identify the requirement of a combination of many techniques and inference mechanism to achieve the accuracy for the problem domain. This research attempted to examine those techniques, and to apply them to implement a Cognitive Hybrid Sentence Modeling and Analyzer. The purpose of developing this system is to facilitate people who face the problem of using English language in daily life.
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
Hamalt genetics based peer to-peer network architecture to encourage the coo...csandit
Since its inception, Internet has grown tremendously not only in the size of its customers but
also with the technology used behind to run it. For the well ex-istence and proper development
of Peer-to-Peer Networks, all nodes in the overlay must be cooperative and donate their
resources for any other peer. The paper dis-cusses the reason of peers being selfish, causes of
selfish peers and the methods used so far to resolve selfish peers problem. A Genetic Algorithm
based solution has been proposed in this paper that solves the selfish nodes problem in Peer-to-
Peer Networks and that also encourages the cooperation among all nodes in the overlay. An
architecture HAMALT is proposed in this paper for disseminating altruism among the peers.
HAMALT : GENETICS BASED PEER-TOPEER NETWORK ARCHITECTURE TO ENCOURAGE THE COO...cscpconf
Since its inception, Internet has grown tremendously not only in the size of its customers but also with the technology used behind to run it. For the well ex-istence and proper development
of Peer-to-Peer Networks, all nodes in the overlay must be cooperative and donate their resources for any other peer. The paper dis-cusses the reason of peers being selfish, causes of
selfish peers and the methods used so far to resolve selfish peers problem. A Genetic Algorithm based solution has been proposed in this paper that solves the selfish nodes problem in Peer-toPeer Networks and that also encourages the cooperation among all nodes in the overlay. An architecture HAMALT is proposed in this paper for disseminating altruism among the peers.
it is related to Computer Graphics Subject.in this ppt we describe what is 2D Transformation, Translation, Rotation, Scaling : Uniform Scaling,Non-uniform Scaling ;Reflection,Shear,Composite Transformations
it related to System Programming Subject.it describe the what is Interpreter?,Comparison between Interpreters and Compilers,Benefits Of Interpreter and Basic of JVM.
It is related to Analysis and Design Of Algorithms Subject.Basically it describe basic of topological sorting, it's algorithm and step by step process to solve the example of topological sort.
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.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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.
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
1. A. D. Patel Institute Of Technology
Artificial Intelligence [2180703] : A. Y. 2019-20
Semantic Net
Prepared By :
Dhruv V. Shah
B.E. (IT) Sem - VIII
Guided By :
Dr. Narendrasinh Chauhan
(Head Of IT , ADIT)
Department Of Information Technology
A.D. Patel Institute Of Technology (ADIT)
New Vallabh Vidyanagar , Anand , Gujarat
1
ALA Presentation
09/ 04 / 2020
3. Introduction
What is Semantic Net ?
Semantic networks are alternative of predicate logic for knowledge
representation.
In Semantic networks, we can represent our knowledge in the form of
graphical networks.
This network consists of nodes representing objects and arcs which describe
the relationship between those objects.
Nodes are sometimes referred to as objects. Nodes are to represent
physical objects, concepts, or situation.
Arcs as links or edges. The links are used to express relationships.
It is also known as associative net due to the association of one node with
other.
Semantic networks are easy to understand and can be easily extended.
3
4. Count…
This representation consist of mainly two types of relations:
a) IS-A relation (Inheritance).
b) A-Kind-of relation (AKO).
4
a) IS-A relation :
IS-A means “is an instance of” and refers to a specific member of a class.
A class is related to the mathematical concept of a set in that it refers to a
group of objects.
Ex. : {3,bat, blue, t-shirt, art}
b) A-Kind-of relation :
The AKO relation is used to relate one class to another.
AKO relates generic nodes to generic nodes while the IS-A relates an
instance or individual to a generic class.
5. Simple Example of Semantic net
Following are some statements which we need to represent in the form of nodes
and arcs.
Statements :
1) Jerry is a cat.
2) Jerry is a mammal.
3) Jerry is owned by Jay.
4) Jerry is white colored.
5) All Mammals are animal.
6) Jerry likes cheese.
In the above diagram, we have represented the different type of knowledge in
the form of nodes and arcs. Each object is connected with another object by
some relation.
5
Jerry
Cat
is-a
mammal
is-a
jay
is-owned
white
is-colored
animal
is-a
cheese
likes
6. Kinds of Semantic Nets
Definitional Networks : Emphasize the subtype or is-a relation between a
concept type and a newly defined subtype.
6
7. Kinds of Semantic Nets
Assertional Networks : Designed to assert propositions.
7
Implicational Networks : Uses implication as the primary relation for
connecting nodes.
8. Kinds of Semantic Nets
Learning Networks : Networks that build or extend their representations by
acquiring knowledge from examples.
8
Executable Networks : Contain mechanisms that can cause some change to the
network itself.
9. Advantages of Semantic network
1) Semantic networks are a natural representation of knowledge.
2) Semantic networks convey meaning in a transparent manner.
3) These networks are simple and easily understandable.
4) Efficient.
5) Translatable to PROLOG w/o difficulty.
9
10. Disadvantages of Semantic network
1) Semantic networks take more computational time at runtime as we need to
traverse the complete network tree to answer some questions. It might be
possible in the worst case scenario that after traversing the entire tree, we find
that the solution does not exist in this network.
2) These types of representations are inadequate as they do not have any
equivalent quantifier, e.g., for all, for some, none, etc.
3) Semantic networks do not have any standard definition for the link names.
4) These networks are not intelligent and depend on the creator of the system.
5) Semantic networks try to model human-like memory to store the information,
but in practice, it is not possible to build such a vast semantic network.
10