The document describes various heuristic search techniques including best first search, A* search, and an example of applying A* search to find the lowest cost path between initial and goal nodes in a graph. Key points:
- A* search uses both path cost (g(n)) and heuristic estimate of distance to goal (h(n)) to calculate the total cost (f(n)) of expanding each node.
- It maintains OPEN and CLOSED lists to track explored and unexplored nodes. The lowest f(n) node in OPEN is selected for expansion at each step.
- Expansion may cause nodes already in CLOSED to be moved back to OPEN if a lower cost path to that node is
The document describes various heuristic search techniques including best first search, A* search, and their algorithms. A* search is a special case of best first search that uses an evaluation function f(n)=g(n)+h(n) where g(n) is cost from start to node n and h(n) is heuristic estimate of cost from n to goal. The A* algorithm maintains OPEN and CLOSED lists, calculates f scores, and expands the lowest f node at each step until reaching the goal node. A node may be moved from CLOSED to OPEN if revisiting it leads to a lower path cost.
Lecture 21 problem reduction search ao star searchHema Kashyap
The AO* search algorithm is used to find optimal solutions for AND/OR search problems. It uses two arrays (OPEN and CLOSE) and a heuristic function h(n) to estimate the cost to reach the goal. The algorithm selects the most promising node from OPEN, expands it to find successors, and calculates their h(n) values, adding them to OPEN. It continues until the start node is marked as solved or unsolvable. AO* finds optimal solutions but can be inefficient for unsolvable problems compared to other algorithms.
This document discusses problem solving strategies in artificial intelligence, including representing problems, search techniques, and informed search strategies. It covers representing problems as state spaces or AND/OR graphs, basic uninformed search algorithms like breadth-first and depth-first search, and informed search techniques such as best-first search and the A* algorithm. Examples of computing heuristics and evaluating states are provided for problems like the 8-puzzle and missionaries and cannibals puzzle.
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 document contains instructions for Assignment 4 of the MA244 Analysis III module. It states that:
- 15% of the module credit comes from 4 assignments due on Mondays in weeks 4, 6, 8, and 10. Each assignment will be marked out of 25 based on answers to 3 randomly chosen 'B' questions and 1 'A' question.
- All questions for the assignment are due by December 7th, 2015 at 3pm and should include the student's name, department, and supervisor/TA. Work should be submitted in the appropriate location based on whether the student is in Maths or another department.
- The assignment contains 10 questions ranging from 0.1 to 4, with
Composition of functions g◦f means applying the function g to the output of f. You apply f first to get an output in its range, then apply g to that output. For example, if f maps x to y, then g◦f maps x to g(y). The composition only includes values where both functions are defined, staying within their domains and ranges. An example composition is given of functions f: A→B and g: B→C, showing how to follow the inputs and outputs through each function to determine the composition g◦f.
This presentation provides an introduction to Galois fields, which are finite fields with a prime number of elements. The objectives are to discuss preliminaries like sets and groups, introduce Galois fields and provide examples, discuss related theorems, and describe the computational approach. A sample computation in FORTRAN verifies the theorem that any element in a Galois field can be expressed as the sum of two squares.
The document discusses AND/OR graphs and the AO* algorithm for searching AND/OR trees. Some problems can be represented as having subgoals that can be achieved simultaneously or independently (AND) or as OR options. The AO* algorithm extends A* search to AND/OR trees. It examines multiple nodes simultaneously, selecting the most promising path and expanding nodes to generate successors. It computes heuristic values (h) for nodes and propagates new information up the graph as the search progresses until a solution is found or all paths are determined to be unsolvable. An example demonstrates how AO* searches an AND/OR graph and labels nodes as it proceeds.
The document describes various heuristic search techniques including best first search, A* search, and their algorithms. A* search is a special case of best first search that uses an evaluation function f(n)=g(n)+h(n) where g(n) is cost from start to node n and h(n) is heuristic estimate of cost from n to goal. The A* algorithm maintains OPEN and CLOSED lists, calculates f scores, and expands the lowest f node at each step until reaching the goal node. A node may be moved from CLOSED to OPEN if revisiting it leads to a lower path cost.
Lecture 21 problem reduction search ao star searchHema Kashyap
The AO* search algorithm is used to find optimal solutions for AND/OR search problems. It uses two arrays (OPEN and CLOSE) and a heuristic function h(n) to estimate the cost to reach the goal. The algorithm selects the most promising node from OPEN, expands it to find successors, and calculates their h(n) values, adding them to OPEN. It continues until the start node is marked as solved or unsolvable. AO* finds optimal solutions but can be inefficient for unsolvable problems compared to other algorithms.
This document discusses problem solving strategies in artificial intelligence, including representing problems, search techniques, and informed search strategies. It covers representing problems as state spaces or AND/OR graphs, basic uninformed search algorithms like breadth-first and depth-first search, and informed search techniques such as best-first search and the A* algorithm. Examples of computing heuristics and evaluating states are provided for problems like the 8-puzzle and missionaries and cannibals puzzle.
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 document contains instructions for Assignment 4 of the MA244 Analysis III module. It states that:
- 15% of the module credit comes from 4 assignments due on Mondays in weeks 4, 6, 8, and 10. Each assignment will be marked out of 25 based on answers to 3 randomly chosen 'B' questions and 1 'A' question.
- All questions for the assignment are due by December 7th, 2015 at 3pm and should include the student's name, department, and supervisor/TA. Work should be submitted in the appropriate location based on whether the student is in Maths or another department.
- The assignment contains 10 questions ranging from 0.1 to 4, with
Composition of functions g◦f means applying the function g to the output of f. You apply f first to get an output in its range, then apply g to that output. For example, if f maps x to y, then g◦f maps x to g(y). The composition only includes values where both functions are defined, staying within their domains and ranges. An example composition is given of functions f: A→B and g: B→C, showing how to follow the inputs and outputs through each function to determine the composition g◦f.
This presentation provides an introduction to Galois fields, which are finite fields with a prime number of elements. The objectives are to discuss preliminaries like sets and groups, introduce Galois fields and provide examples, discuss related theorems, and describe the computational approach. A sample computation in FORTRAN verifies the theorem that any element in a Galois field can be expressed as the sum of two squares.
The document discusses AND/OR graphs and the AO* algorithm for searching AND/OR trees. Some problems can be represented as having subgoals that can be achieved simultaneously or independently (AND) or as OR options. The AO* algorithm extends A* search to AND/OR trees. It examines multiple nodes simultaneously, selecting the most promising path and expanding nodes to generate successors. It computes heuristic values (h) for nodes and propagates new information up the graph as the search progresses until a solution is found or all paths are determined to be unsolvable. An example demonstrates how AO* searches an AND/OR graph and labels nodes as it proceeds.
The document discusses finite fields and related algebraic concepts. It begins by defining groups, rings, and fields. It then focuses on finite fields, particularly GF(p) fields consisting of integers modulo a prime p. It discusses finding multiplicative inverses in such fields using the extended Euclidean algorithm. As an example, it finds the inverse of 550 modulo 1759.
This document provides information about the A* search algorithm. It begins with an overview of A* search and its advantages and disadvantages. It then discusses the concepts of admissibility and consistency which are required for A* search to be optimal. The standard A* algorithm is presented involving maintaining OPEN and CLOSE lists. An example of running A* search on a graph is provided. Finally, it discusses how to make A* search admissible by ensuring the heuristic function underestimates the actual cost rather than overestimating it.
The document describes hill climbing, a local search algorithm that starts with an initial node and moves to successors that are closer to the goal. It can get stuck in local maxima and plateaus. Backtracking and big jumps are proposed solutions. Best-first search evaluates successors and adds them to a priority queue based on estimated distance to the goal. A* uses an evaluation function plus path cost as a fitness metric. AND/OR graphs represent achieving subgoals simultaneously or independently. AND/OR best-first search expands the most promising path until a solution is found or the starting node is deemed unsolvable.
The document discusses simulating the construction of finite fields using Maple software. It presents Maple code that constructs the finite field F64 as the quotient ring F2[x]/(f(x)) where f(x) is the primitive polynomial x6 + x + 1. It also constructs the subfield F8 of F64. The code is intended to help students visualize finite fields before learning the theoretical foundations of the topic.
This document discusses the distance formula and how to find the distance between two points on a coordinate plane. It begins by defining key concepts like horizontal and vertical distance. Examples are provided to demonstrate using the distance formula to calculate distances. The document then shows an example application problem involving finding the distance between two cities on a map. It concludes by generalizing the process for finding distances between any two points whether aligned horizontally/vertically or not.
* Find the distance between two points
* Find the midpoint of two given points
* Find the coordinates of an endpoint given one endpoint and a midpoint
* Find the coordinates of a point a fractional distance from one end of a segment
The student is able to (I can):
• Find the midpoint of two given points.
• Find the coordinates of an endpoint given one endpoint
and a midpoint.
• Find the distance between two points.
College Algebra MATH 107Record your answers and work on th.docxclarebernice
This document provides information and instructions for a College Algebra exam. It includes 30 problems - 12 multiple choice and 18 short answer. The multiple choice section covers topics like domains, ranges, functions, and graphs. The short answer section requires showing work for problems involving algebra, functions, equations, and other concepts. Students may use their text and a calculator during the open book exam.
This document summarizes various informed search algorithms including greedy best-first search, A* search, and memory-bounded heuristic search algorithms like recursive best-first search and simple memory-bounded A* search. It discusses how heuristics can be used to guide the search towards optimal solutions more efficiently. Admissible and consistent heuristics are defined and their role in guaranteeing optimality of A* search is explained. Methods for developing effective heuristic functions are also presented.
The document provides information about algebraic expressions and polynomials. It begins by explaining that algebra uses a language of numbers, variables, and symbols to represent quantitative relationships. The document then covers topics like equivalent expressions, simplifying expressions, evaluating expressions, and different types of polynomials. It aims to help students acquire skills in performing operations with polynomials and using those skills to simplify, evaluate, and solve algebraic expressions and problems.
- Ethernet was first created by Robert Metcalfe and standardized by IEEE as 802.3. It uses Manchester encoding and CSMA/CD.
- Fast Ethernet (802.3u) was developed to transmit data 10 times faster than standard Ethernet at 100 Mbps, while still being backward compatible.
- Gigabit Ethernet (802.3z) further increased the data rate to 1000 Mbps and supports both full-duplex and half-duplex modes using switches and hubs.
Transmission control protocol ...............................SwatiHans10
The document discusses the Transmission Control Protocol (TCP) which operates at the transport layer of the OSI model. TCP provides reliable, connection-oriented data transmission through the use of sequence numbers, acknowledgments, and retransmissions to ensure packets are delivered correctly. It establishes connections using a 3-way handshake and closes connections through a 4-way handshake. TCP uses port numbers to identify applications at each end of the connection and implements flow and congestion control to regulate data transfer rates.
Linear search is a sequential search algorithm that checks each element of an array until the target element is found. It has a worst-case time complexity of O(n) where n is the number of elements. Binary search is a divide and conquer algorithm that compares the target to the middle element of a sorted array, eliminating half of the remaining elements with each comparison. It has a time complexity of O(log n). Common sorting algorithms like bubble sort, insertion sort, and selection sort have a time complexity of O(n^2) as they may require up to n^2 comparisons in the worst case.
Analytics platforms like Google Analytics provide quantitative user data across devices and platforms by tracking metrics like sessions and bounce rates in reports to understand website usage. Google Analytics also collects event-based data from websites and apps to analyze user behavior, and integrates with other Google tools to combine all marketing data in a single location.
The document discusses Key Performance Indicators (KPIs), which are quantifiable measures used to evaluate factors that are crucial to the success of an organization. KPIs help measure performance against goals. The document outlines what makes a good KPI, such as being business-aligned, actionable, realistic and measurable. It also discusses how to develop KPIs using the SMART framework and provides examples of KPIs for different business functions like IT, marketing, customer service, sales and finance.
The document discusses various strategies and tools for collecting data in evaluations. It describes both quantitative and qualitative approaches, and notes that the best approach depends on factors like the information needed, resources available, and complexity of the data. It provides guidelines for collecting data and discusses the advantages and challenges of various tools, including surveys, interviews, focus groups, observation, diaries, expert judgment, and more. The goal is to choose appropriate and multiple methods to ensure accurate and comprehensive data collection.
Weak slot and filler structures are knowledge representation structures that organize objects into classes with attributes and values. They allow for property inheritance along "isa" and "instance" links. Semantic nets represent information as nodes connected by labeled arcs, where nodes are objects/attribute values and arcs are relationships. Frames represent entities as collections of slots (attributes) and associated values (fillers). Frames, semantic nets, and slot-filler structures allow knowledge to be organized and property inheritance to be performed, supporting reasoning.
This document discusses weak slot-and-filler knowledge representation structures. It describes how they organize objects into classes with attributes and values to support property inheritance. Semantic nets are provided as an example, where nodes represent objects/attribute values and arcs represent relationships. Frames are also discussed as a type of weak slot-and-filler structure that group attributes into slots and associated values. The document notes how slot-and-filler structures allow for monotonic and non-monotonic reasoning. It also covers issues like tangled hierarchies and resolving conflicts through inferential distance in property inheritance.
The document discusses issues in knowledge representation and predicate logic. It covers important attributes like "instance" and "isa" that support property inheritance. It also discusses the relationship between attributes, choosing an appropriate level of granularity, and finding the right knowledge structure. The key knowledge representation methods covered are logic, production rules, semantic nets, and frames. Predicate logic represents facts, objects, and relations using variables, predicates, functions, and quantifiers to make inferences. Horn clauses represent rules using predicates and quantifiers.
This document discusses multiplexing and spreading techniques. It describes multiplexing as a set of techniques that allows simultaneous transmission of multiple signals over a single data link to maximize bandwidth utilization. Frequency-division multiplexing (FDM), wavelength-division multiplexing (WDM), and time-division multiplexing (TDM) are discussed as categories of multiplexing. Spread spectrum is described as combining signals from different sources to prevent eavesdropping and jamming through redundancy. Frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum synchronous (DSSS) are introduced as spread spectrum techniques. Examples of applying these concepts are provided through diagrams and calculations.
The document discusses the entity-relationship (E-R) model for conceptual database design. It describes how a database can be modeled as a collection of entities and relationships between entities. Entity sets contain entities of the same type, and relationship sets define associations among entity sets. The document outlines key E-R modeling concepts such as attributes, keys, cardinalities, participation constraints, and weak entities. It also discusses how to represent an E-R design using an E-R diagram.
The document discusses finite fields and related algebraic concepts. It begins by defining groups, rings, and fields. It then focuses on finite fields, particularly GF(p) fields consisting of integers modulo a prime p. It discusses finding multiplicative inverses in such fields using the extended Euclidean algorithm. As an example, it finds the inverse of 550 modulo 1759.
This document provides information about the A* search algorithm. It begins with an overview of A* search and its advantages and disadvantages. It then discusses the concepts of admissibility and consistency which are required for A* search to be optimal. The standard A* algorithm is presented involving maintaining OPEN and CLOSE lists. An example of running A* search on a graph is provided. Finally, it discusses how to make A* search admissible by ensuring the heuristic function underestimates the actual cost rather than overestimating it.
The document describes hill climbing, a local search algorithm that starts with an initial node and moves to successors that are closer to the goal. It can get stuck in local maxima and plateaus. Backtracking and big jumps are proposed solutions. Best-first search evaluates successors and adds them to a priority queue based on estimated distance to the goal. A* uses an evaluation function plus path cost as a fitness metric. AND/OR graphs represent achieving subgoals simultaneously or independently. AND/OR best-first search expands the most promising path until a solution is found or the starting node is deemed unsolvable.
The document discusses simulating the construction of finite fields using Maple software. It presents Maple code that constructs the finite field F64 as the quotient ring F2[x]/(f(x)) where f(x) is the primitive polynomial x6 + x + 1. It also constructs the subfield F8 of F64. The code is intended to help students visualize finite fields before learning the theoretical foundations of the topic.
This document discusses the distance formula and how to find the distance between two points on a coordinate plane. It begins by defining key concepts like horizontal and vertical distance. Examples are provided to demonstrate using the distance formula to calculate distances. The document then shows an example application problem involving finding the distance between two cities on a map. It concludes by generalizing the process for finding distances between any two points whether aligned horizontally/vertically or not.
* Find the distance between two points
* Find the midpoint of two given points
* Find the coordinates of an endpoint given one endpoint and a midpoint
* Find the coordinates of a point a fractional distance from one end of a segment
The student is able to (I can):
• Find the midpoint of two given points.
• Find the coordinates of an endpoint given one endpoint
and a midpoint.
• Find the distance between two points.
College Algebra MATH 107Record your answers and work on th.docxclarebernice
This document provides information and instructions for a College Algebra exam. It includes 30 problems - 12 multiple choice and 18 short answer. The multiple choice section covers topics like domains, ranges, functions, and graphs. The short answer section requires showing work for problems involving algebra, functions, equations, and other concepts. Students may use their text and a calculator during the open book exam.
This document summarizes various informed search algorithms including greedy best-first search, A* search, and memory-bounded heuristic search algorithms like recursive best-first search and simple memory-bounded A* search. It discusses how heuristics can be used to guide the search towards optimal solutions more efficiently. Admissible and consistent heuristics are defined and their role in guaranteeing optimality of A* search is explained. Methods for developing effective heuristic functions are also presented.
The document provides information about algebraic expressions and polynomials. It begins by explaining that algebra uses a language of numbers, variables, and symbols to represent quantitative relationships. The document then covers topics like equivalent expressions, simplifying expressions, evaluating expressions, and different types of polynomials. It aims to help students acquire skills in performing operations with polynomials and using those skills to simplify, evaluate, and solve algebraic expressions and problems.
- Ethernet was first created by Robert Metcalfe and standardized by IEEE as 802.3. It uses Manchester encoding and CSMA/CD.
- Fast Ethernet (802.3u) was developed to transmit data 10 times faster than standard Ethernet at 100 Mbps, while still being backward compatible.
- Gigabit Ethernet (802.3z) further increased the data rate to 1000 Mbps and supports both full-duplex and half-duplex modes using switches and hubs.
Transmission control protocol ...............................SwatiHans10
The document discusses the Transmission Control Protocol (TCP) which operates at the transport layer of the OSI model. TCP provides reliable, connection-oriented data transmission through the use of sequence numbers, acknowledgments, and retransmissions to ensure packets are delivered correctly. It establishes connections using a 3-way handshake and closes connections through a 4-way handshake. TCP uses port numbers to identify applications at each end of the connection and implements flow and congestion control to regulate data transfer rates.
Linear search is a sequential search algorithm that checks each element of an array until the target element is found. It has a worst-case time complexity of O(n) where n is the number of elements. Binary search is a divide and conquer algorithm that compares the target to the middle element of a sorted array, eliminating half of the remaining elements with each comparison. It has a time complexity of O(log n). Common sorting algorithms like bubble sort, insertion sort, and selection sort have a time complexity of O(n^2) as they may require up to n^2 comparisons in the worst case.
Analytics platforms like Google Analytics provide quantitative user data across devices and platforms by tracking metrics like sessions and bounce rates in reports to understand website usage. Google Analytics also collects event-based data from websites and apps to analyze user behavior, and integrates with other Google tools to combine all marketing data in a single location.
The document discusses Key Performance Indicators (KPIs), which are quantifiable measures used to evaluate factors that are crucial to the success of an organization. KPIs help measure performance against goals. The document outlines what makes a good KPI, such as being business-aligned, actionable, realistic and measurable. It also discusses how to develop KPIs using the SMART framework and provides examples of KPIs for different business functions like IT, marketing, customer service, sales and finance.
The document discusses various strategies and tools for collecting data in evaluations. It describes both quantitative and qualitative approaches, and notes that the best approach depends on factors like the information needed, resources available, and complexity of the data. It provides guidelines for collecting data and discusses the advantages and challenges of various tools, including surveys, interviews, focus groups, observation, diaries, expert judgment, and more. The goal is to choose appropriate and multiple methods to ensure accurate and comprehensive data collection.
Weak slot and filler structures are knowledge representation structures that organize objects into classes with attributes and values. They allow for property inheritance along "isa" and "instance" links. Semantic nets represent information as nodes connected by labeled arcs, where nodes are objects/attribute values and arcs are relationships. Frames represent entities as collections of slots (attributes) and associated values (fillers). Frames, semantic nets, and slot-filler structures allow knowledge to be organized and property inheritance to be performed, supporting reasoning.
This document discusses weak slot-and-filler knowledge representation structures. It describes how they organize objects into classes with attributes and values to support property inheritance. Semantic nets are provided as an example, where nodes represent objects/attribute values and arcs represent relationships. Frames are also discussed as a type of weak slot-and-filler structure that group attributes into slots and associated values. The document notes how slot-and-filler structures allow for monotonic and non-monotonic reasoning. It also covers issues like tangled hierarchies and resolving conflicts through inferential distance in property inheritance.
The document discusses issues in knowledge representation and predicate logic. It covers important attributes like "instance" and "isa" that support property inheritance. It also discusses the relationship between attributes, choosing an appropriate level of granularity, and finding the right knowledge structure. The key knowledge representation methods covered are logic, production rules, semantic nets, and frames. Predicate logic represents facts, objects, and relations using variables, predicates, functions, and quantifiers to make inferences. Horn clauses represent rules using predicates and quantifiers.
This document discusses multiplexing and spreading techniques. It describes multiplexing as a set of techniques that allows simultaneous transmission of multiple signals over a single data link to maximize bandwidth utilization. Frequency-division multiplexing (FDM), wavelength-division multiplexing (WDM), and time-division multiplexing (TDM) are discussed as categories of multiplexing. Spread spectrum is described as combining signals from different sources to prevent eavesdropping and jamming through redundancy. Frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum synchronous (DSSS) are introduced as spread spectrum techniques. Examples of applying these concepts are provided through diagrams and calculations.
The document discusses the entity-relationship (E-R) model for conceptual database design. It describes how a database can be modeled as a collection of entities and relationships between entities. Entity sets contain entities of the same type, and relationship sets define associations among entity sets. The document outlines key E-R modeling concepts such as attributes, keys, cardinalities, participation constraints, and weak entities. It also discusses how to represent an E-R design using an E-R diagram.
Cloud computing can be applied in several domains:
1) Large organizations like NASA and CERN are using private clouds to provide resources to thousands of researchers globally in a cost-effective manner. NASA's Nebula cloud allows scientists to run climate models remotely.
2) Cloud platforms can be mashed up to provide both scalability and agility. For example, a mashup combines Google App Engine for web services and Amazon EC2 for parallel computing.
3) Cloud computing supports the Internet of Things by providing resources for processing and analyzing data from billions of connected devices.
This document discusses various 3D transformations including translation, rotation, scaling, reflection, and shearing. It provides the transformation matrices for each type of 3D transformation. It also discusses combining multiple transformations through composite transformations by multiplying the matrices in sequence from right to left.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
2. Heuristic Search Techniques
1. Generate & Test
2. Hill Climbing
Simple Hill Climbing
Steepest Ascent Hill Climbing
3. Best First Search & A*
4. Problem Reduction & AO*
5. Constraint Satisfaction
6. Means – Ends Analysis
3. A best-first search depends on the use of a heuristic
to select the most promising path to a goal node.
Best first search selects the next node based on the
best estimate out of all the estimates of the heuristic
function calculated so far – this is where Best-First
Search differs from Hill Climbing.
Greedy best-first search tries to expand the node that
is closest to the goal, that this is likely to lead to a
solution quickly.
Use Priority queue for implementation
Thus, it evaluates nodes by using just the heuristic
function; that is, f (n) = h(n)
4. Step 1: Place the starting node into the OPEN list.
Step 2: If the OPEN list is empty, Stop and return failure.
Step 3: Remove the node n, from the OPEN list which has the lowest
value of h(n), and places it in the CLOSED list.
Step 4: Expand the node n, and generate the successors of node n.
Step 5: Check each successor of node n, and find whether any node is
a goal node or not. If any successor node is goal node, then return
success and terminate the search, else proceed to Step 6.
Step 6: For each successor node, algorithm checks for evaluation
function f(n), and then check if the node has been in either OPEN or
CLOSED list. If the node has not been in both list, then add it to the
OPEN list.
Step 7: Return to Step 2.
5. S is initial state
G is goal state
H(N) heuristic function is the estimated
straight line distance from node n to goal
6. Expand the nodes of S
Initialization: Open= [S], Closed= [ ]
Open= [B,A], Closed= [S]
Open=[A], Closed=[S, B]
Open=[F,E,A],Closed= [S, B]
Open=[E,A], Closed=[S, B, F]
Open=[G,E,I,A], Closed=[S, B, F]
Open=[E,I,A], Closed=[S, B, F, G]
Hence the final solution path will be:
S----> B----->F----> G
7. A specialization of Best-First Search
The procedure followed by A* Search is:
Along a path to the goal, at each node, the A*
algorithm generates all the child nodes,
For each child node, A* computes the estimate of
the distance (cost) from the start node to a goal
node through each of the children,
A* selects the child node which has the minimum
cost and expands it. The process than repeats for
this selected node until the goal is found or the
search ends in failure.
8. The total cost function f(n) used by A*, for the
current node n during the search process is given
by
OPEN LIST stores the nodes which have been
visited but not expanded and are available (are
“open”) for expansion, that is, their child nodes are
yet to be identified.
CLOSED LIST stores the nodes that have been
visited and are not available (are “closed”) for
further expansion.
9. 1. Initialize: Set OPEN={s}, CLOSED={ },
g(s)=0 and f(s)=h(s).
2. Fail: if OPEN={ }; terminate and fail.
3. Select: Select the minimum cost state, n
from OPEN. Save n in CLOSED.
4. Terminate: If n G, where G is the set of
goal states, terminate with success and
return f(n).
10. 5. Expand: For each child, m, of n
6. IF m G, where G is the set of goal states, terminate
with success and return f(n).
if m [OPEN CLOSED], CALCULATE F(M)
Set f(m) = g(m) + h(m)
Insert m in OPEN.
7. Return: Return to Step 2.
11.
12. Expand the nodes of A
Step1 :Initialization: Open= [A], Closed= [ ]
Step 2:Remove A and move adjacent of A i.e B & F in open and
calculates f(B) and f(F).
f(B) = 6 + 8 = 14
f(F) = 3 + 6 = 9
So,
Open= [F,B], Closed= [A]
Step3: Since f(F) < f(B), so it decides to remove node F and move
its adjacent in Open G,H
f(G) = (3+1) + 5 = 9
f(H) = (3+7) + 3 = 13
So,
Open= [G,H,B], Closed= [A,F]
Path- A → F
13. Step4: Since f(G) < f(H), so it decides to remove
node G and move its adjacent in Open I and
calculate f(I)
f(I) = (7) + 1 = 8
So,
Open= [I,H,B], Closed= [A,F,G]
Path- A F G
Step5: Since node I was only adjacent to node G ,so it
decides to remove node I and move its adjacent in Open E
and J and calculate f(E) and f(J)
f(E) = (12) + 3 = 15
f(J)= (10) +0=10
So,Open= [J,E,H,B], Closed= [A,F,G,I] Path- A → F → G → I
14. Step4: Since f(J) < f(E), so it decides to remove
node J as J is goal node also and exit
So,
So,Open= [E,H,B], Closed= [A,F,G,I,J]
Path- A → F → G → I → J
17. OPEN
A(5)
B(7) C(25)
CLOSED
A(5)
A(5) B(7)
Expand A to obtain B and C. B does not belong to [OPEN union CLOSED].
Therefore, set g(B)=g(A) + C(A, B) = 0 + 3 = 3.
And, set f(B) = g(B) + h(B) = 3 + 4 = 7. Place B on OPEN.
Similarly, g(C)=0 + 2 = 2 and f(C)=0+23=25. Place C in OPEN. Return to step 2.
Minimum cost state in OPEN is B. Put B in CLOSED. B does not belong to G.
18. OPEN
A(5)
B(7) C(25)
C(25) D(9)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
Expand B to obtain D. D does not belong to [OPEN union CLOSED].
Therefore, set g(D)=g(B) + C(B, D) = 3 + 4 = 7.
And, set f(D) = g(D) + h(D) = 7 + 2 = 9. Place D in OPEN. Return to step 2.
Minimum cost state in OPEN is D. Put D in CLOSED. D does not belong to G.
19. OPEN
A(5)
B(7) C(25)
C(25) D(9)
C(25) E(11)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
A(5) B(7) D(9) E(11)
Expand D to obtain E. E does not belong to [OPEN union CLOSED].
Therefore, set g(E)=g(D) + C(D, E) = 7 + 1 = 8.
And, set f(E) = g(E) + h(E) = 8 + 3 = 11. Place E in OPEN. Return to step 2.
Minimum cost state in OPEN is E. Put E in CLOSED. E does not belong to G.
20. OPEN
A(5)
B(7) C(25)
C(25) D(9)
C(25) E(11)
C(25) F(28)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
A(5) B(7) D(9) E(11)
A(5) B(7) D(9) E(11) C(25)
Expand E to obtain F. F does not belong to [OPEN union CLOSED].
Therefore, set g(F)=g(E) + C(E, F) = 8 + 20 = 28.
And, set f(F) = g(F) + h(F) = 28 + 0 = 28. Place F in OPEN. Return to step 2.
Minimum cost state in OPEN is C. Put C in CLOSED. C does not belong to G.
21. OPEN
A(5)
B(7) C(25)
C(25) D(9)
C(25) E(11)
C(25) F(28)
F(28) D(7)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
A(5) B(7) D(9) E(11)
A(5) B(7) E(11 C(25)
A(5) B(7) D(9) E(11) C(25)
Expand C to obtain D. D belongs to [OPEN union CLOSED] as it is in CLOSED.
Therefore, set g(D)=min{g(D), g(C) + C(C, D)} = min{7, 2 + 3} = 5.
And, set f(D) = g(D) + h(D) = 5 + 2 = 7. As f(D) has decreased from 9 to 7, and D
belongs to CLOSED, therefore move D from CLOSED to OPEN. Return to step 2.
22. OPEN
A(5)
B(7) C(25)
C(25) D(9)
C(25) E(11)
C(25) F(28)
F(28) D(7)
F(28) E(9)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
A(5) B(7) D(9) E(11)
A(5) B(7) E(11 C(25)
A(5) B(7) E(11 C(25) D(7)
A(5) B(7) C(25 D(7)
Minimum cost state in OPEN is D. Put D in CLOSED. D does not belong to G.
Expand D to obtain E. E belongs to [OPEN union CLOSED] as it is in CLOSED.
Therefore, set g(E)=min{g(E), g(D) + C(D, E)} = min{8, 5 + 1} = 6.
And, set f(E) = g(E) + h(E) = 6 + 3 = 9. As f(E) has decreased from 11 to 9, and E
belongs to CLOSED, therefore move E from CLOSED to OPEN. Return to step 2.
F(28)
23. OPEN
A(5)
B(7) C(25)
C(25) D(9)
C(25) E(11)
C(25) F(28)
F(28) D(7)
F(28) E(9)
F(28)
CLOSED
A(5)
A(5) B(7) D(9)
A(5) B(7)
A(5) B(7) D(9) E(11)
A(5) B(7) E(11 C(25)
A(5) B(7) C(25) D(7)
A(5) B(7) C(25) D(7) E(9)
Minimum cost state in OPEN is E. Put E in CLOSED. E does not belong to G.
F(26)
Expand E to obtain F. F belongs to [OPEN union CLOSED] as it is in OPEN.
Therefore, set g(F)=min{g(F), g(E) + C(E, F)} = min{28, 6 + 20} = 26.
And, set f(F) = g(F) + h(F) = 26 + 0 = 26. As f(F) has decreased from 28 to 26, but
F does not belong to CLOSED, it is already in OPEN. Replace it. Return to step 2.
Minimum cost state in OPEN is F with the cost 26. F belongs to G. Therefore,
return f(F)=26. This is the minimum cost of reaching the goal node F.
24. When will a node have to be moved from
CLOSED to OPEN?
This is required only in the case when the data
structure being used for the search is a graph
rather than a tree.
In the case of a graph, the moving of a node from
CLOSED to OPEN indicates a backtracking and
that the path followed till the node being moved
is not the best path and there are other
alternative paths which may be better.