1) The document discusses various search techniques used in artificial intelligence including uninformed searches like breadth-first search and depth-first search as well as informed heuristic searches like greedy best-first search and A* search.
2) It provides examples to illustrate different search techniques including the bridges of Konigsberg problem and traveling salesperson problem.
3) Key concepts discussed include search spaces, heuristic functions, and evaluating search performance based on completeness, optimality, time complexity and space complexity.
Solving problems by searching Informed (heuristics) Searchmatele41
Informed Search – a strategy that uses problem-specific knowledge beyond the definition of the problem itself
Best-First Search – an algorithm in which a node is selected for expansion based on an evaluation function f(n)
Artificial Intelligence lecture notes. AI summarized notes for heuristically informed searches and types of searches in ai ( ai search algorithms ) and machine learning as well, just for reading and may be for self-learning, I think.
Solving problems by searching Informed (heuristics) Searchmatele41
Informed Search – a strategy that uses problem-specific knowledge beyond the definition of the problem itself
Best-First Search – an algorithm in which a node is selected for expansion based on an evaluation function f(n)
Artificial Intelligence lecture notes. AI summarized notes for heuristically informed searches and types of searches in ai ( ai search algorithms ) and machine learning as well, just for reading and may be for self-learning, I think.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
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
Abstract: This PDSG workship introduces basic concepts on Greedy and A-STAR search. Examples are given pictorially, as pseudo code and in Python.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html and http://www.jarrar.info
The lecture covers: Un-informed Search
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123
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
Abstract: This PDSG workship introduces basic concepts on Greedy and A-STAR search. Examples are given pictorially, as pseudo code and in Python.
Level: Fundamental
Requirements: Should have prior familiarity with Graph Search. No prior programming knowledge is required.
Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html and http://www.jarrar.info
The lecture covers: Un-informed Search
Types of inference engines
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification.
Truth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology ClassificationTruth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology ClassificationTruth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology ClassificationTruth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology ClassificationTruth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology ClassificationTruth Maintenance.
Hypothetical Reasoning.
Fuzzy Logic.
Ontology Classification
Our three-eye-alien friend uncovered an impressively complete
and up-to-date family tree tracing all the way back to the ancient
emperor Qin Shi Huang. The alien wants to find a descendant of
this emperor who’s still alive, and could use your advice!
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.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
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.
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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.
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.
2. Motivation
Attempt the end, and never stand to
doubt, nothing’s so hard, but search
will find it out
“Robert Herrick”
3. What we will cover ?
Ideas in searching
Searching tree
representation
Uninformed and informed
search
Game playing search
4. Problem as a State Space
Search
To build as system to solve a particular problem, we
need:
Define the problem: must include precise
specifications ~ initial solution & final solution.
Analyze the problem: select the most important
features that can have an immense impact.
Isolate and represent : convert these important
features into knowledge representation.
Problem solving technique(s): choose the best
technique and apply it to particular problem.
5. The Quest
Typical questions that need to be answered:
Is the problem solver guaranteed to find a solution?
Will the system always terminate or caught in a infinite
loop?
If the solution is found, it is optimal?
What is the complexity of searching process?
How the system be able to reduce searching
complexity?
How it can effectively utilize the representation
paradigm?
6. Important Terms
Search space possible conditions and solutions.
Initial state state where the searching process
started.
Goal state the ultimate aim of searching process.
Problem space “what to solve”
Searching strategy strategy for controlling the
search.
Search tree tree representation of search space,
showing possible solutions from initial state.
7. Example: The Bridges of
Konigsberg Problem
•Classical graph
applications.
•Introduced by Leonhard
Euler.
•Problem: Can a person
walk around the city
crosses each bridge
exactly once?
8. Example: The Bridges of
Konigsberg Problem (cont)
B
D
C
A
b1
b2
b3
b4
b6
b5
b7
Predicates: Connect (B, C, b5)
9. Example: Traveling
Salesperson Problem
•Suppose a salesperson has five cities to visit and them
must return home. Goal find the shortest path to
travel.
B
C
D
E
75
50
100
100
125
125
125
75
50
A
10. Searching for Solution
•Search through state space (explicitly using searching
tree).
•Node expansion :- generating new node related to previous
nodes.
•Concepts:
•State :- conditions in which the node corresponds.
•Parent node :- the superior node
•Path cost :- the cost, from initial to goal state.
•Depth:- number of steps along the path from initial state
15. Measuring Searching
Performance
•The output from problem-solving (searching) algorithm is
either FAILURE or SOLUTION.
•Four ways:
•Completeness : is guaranteed to find a solution?
•Optimality: does it find optimal solution ?
•Time complexity: how long?
•Space complexity: how much memory?
•Complexity : branching factor (b), depth (d), and
max. depth (m)
16. Searching Strategies
•Heuristic search search
process takes place by
traversing search space with
applied rules (information).
•Techniques: Greedy Best
First Search, A* Algorithm
•There is no guarantee that
solution is found.
•Blind search traversing
the search space until the
goal nodes is found (might be
doing exhaustive search).
•Techniques : Breadth First
Uniform Cost ,Depth first,
Interactive Deepening
search.
•Guarantees solution.
17. Blind Search : Breadth First
Search (BFS)
•Strategy ~ search all the nodes expanded at given depth
before any node at next level.
•Concept : First In First Out (FIFO) queue.
•Complete ?: Yes with finite b (branch).
•Complexity:
•Space : similar to complexity – keep nodes in every memory
•Optimal ? = Yes (if cost =1)
19. Blind Search : Depth First
Search (DFS)
•Strategy ~ search all the nodes expanded in deepest path.
•Last In First Out concept.
•Complete ?: No
•Complexity:
•Space : O(bm) – b ; branching factor, m ; max. depth
•Optimality ? : No
21. Blind Search : Iterative
Deepening DFS (ID-DFS)
•Strategy ~ combines DFS with best depth limits.
•Gradually increase the limit; L=0, L=1… and so on.
•Complete ?: Yes (if b is finite)
•Complexity:
•Space :
•Optimality ? : yes (if path costs are all identical)
24. Heuristic Search :
•Important aspect: formation of
heuristic function (h(n)).
•Heuristic function additional
knowledge to guide searching
strategy (short cut).
•Distance: heuristic function
can be straight line distance
(SLD)
A*
B C*
D
E
h(n)=0
h(n)=34
h(n)=24
h(n)=67
h(n)=12
h(n)=9
26. Heuristic Search :Greedy-
Best Search
•Tries to expand the node that is closest to the
goal.
•Evaluates using only heuristic function : f(n) = h(n)
•Possibly lead to the solution very fast.
•Problem ? ~ can end up in sub-optimal solutions
(doesn’t take notice of the distance it travels).
•Complexity and time:
•Complete & optimal ? : No (stuck in infinite loop)
29. Heuristic Search : A*
Algorithm
•Widely known algorithm – (pronounced as “A star”
search).
•Evaluates nodes by combining g(n) “cost to reach
the node” and h(n) “cost to get to the goal”
•f(n) = g(n) + h(n), f(n) estimated cost of the
cheapest solution.
•Complete and optimal ~ since evaluates all paths.
•Time ? ~ a bit time consuming
•Space ? ~ lot of it!
36. Issues in Heuristic Search
•Searching using heuristic function does not solely on
directed solution but the best algorithm to find
shortest path towards goal.
•Admissible attempt to find possible shortest path to
a goal whenever it exists.
•Informedness question in what sense the heuristic
function is better than another.
•Monotonicity question if the best state is
discovered by heuristic search, is there any guarantee
that the same state won’t be found later at lowest
searching cost?
37. References
Cawsey, A. (1998). The Essence of Artificial
Intelligence, Prentice Hall.
Russell, S. and Norvig, P. (2003). Artificial
Intelligence: A Modern Approach, Prentice-
Hall 2nd Edition.