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This document discusses various problem solving agents and search strategies. It describes well defined and non well defined problem types. Examples provided include traveling salesman problem and 8-puzzle. Search strategies covered are uninformed searches like breadth-first, depth-first, uniform cost, iterative deepening and bidirectional search. Their properties like completeness, optimality and time/space complexity are evaluated. Different graph and tree search algorithms are also discussed.

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Chapter3 Search

The document discusses different types of problem-solving agents and search algorithms. It describes single-state, sensorless, contingency, and exploration problem types. It also summarizes common uninformed search strategies like breadth-first search, uniform-cost search, depth-first search, depth-limited search, and iterative deepening search and analyzes their properties in terms of completeness, time complexity, space complexity, and optimality. Examples of problems that can be modeled as state space searches are also provided, like the vacuum world, 8-puzzle, and robotic assembly problems.

CptS 440 / 540 Artificial Intelligence

The document discusses search techniques in artificial intelligence. It defines search as finding a sequence of actions to achieve a goal state. Common problems that use search include problem solving, natural language processing, computer vision, and machine learning. Search involves defining a search space with states, operators to transition between states, an initial state, and a goal test. Popular uninformed search techniques like breadth-first search and depth-first search are explained. The document also introduces informed search techniques like uniform cost search that use cost information to guide the search towards optimal solutions.

Searching

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.

state-spaces29Sep06.ppt

The document discusses problem solving through search. It defines intelligent agents, search problems, and search graphs. Search problems are formulated using states, operators, start states, and goal states. Several search algorithms are introduced, including depth-first search and breadth-first search. Examples of search problems discussed include finding a route from Arad to Bucharest in Romania, the vacuum world problem, the 8-queens problem, and the 8-puzzle problem. The document outlines how to represent these problems as state spaces and formulates them in terms of states, actions, initial states, and goal tests. It also introduces tree search algorithms and strategies for searching state spaces, such as uninformed blind search and informed heuristic search.

Search 1

The document discusses various search algorithms used in artificial intelligence problem solving including breadth-first search, uniform-cost search, depth-first search, iterative deepening depth-first search, and bidirectional search. It provides examples of route finding problems and defines the components of a search problem. It also analyzes and compares the algorithms based on their completeness, time complexity, space complexity, and ability to find optimal solutions.

Chapter 3.pptx

The document summarizes different search strategies for solving problems by searching state spaces, including uninformed strategies like breadth-first search, depth-first search, and iterative deepening search, as well as informed strategies like greedy best-first search and A* search. Breadth-first search expands the shallowest nodes first and is complete and optimal for uniform costs. Depth-first search expands the deepest nodes first but may not terminate. Iterative deepening search runs depth-first search iteratively with increasing depth limits to guarantee completeness. Greedy best-first search uses a heuristic to select nodes but can get stuck in loops. A* search uses both path cost and heuristic cost and is guaranteed to find the optimal solution if the heuristic

Informed search (heuristics)

1) The document discusses various search algorithms including uninformed searches like breadth-first search as well as informed searches using heuristics.
2) It describes greedy best-first search which uses a heuristic function to select the node closest to the goal at each step, and A* search which uses both path cost and heuristic cost to guide the search.
3) Genetic algorithms are introduced as a search technique that generates successors by combining two parent states through crossover and mutation rather than expanding single nodes.

c4.pptx

Artificial intelligence techniques can be used to solve search problems by modeling them as trees. Common search strategies include breadth-first search, depth-first search, uniform cost search, and iterative deepening search. These strategies differ in terms of completeness, optimality, time complexity, and space complexity. More advanced techniques like bidirectional search can improve search efficiency by exploring the problem space from both the initial and goal states simultaneously.

Chapter3 Search

The document discusses different types of problem-solving agents and search algorithms. It describes single-state, sensorless, contingency, and exploration problem types. It also summarizes common uninformed search strategies like breadth-first search, uniform-cost search, depth-first search, depth-limited search, and iterative deepening search and analyzes their properties in terms of completeness, time complexity, space complexity, and optimality. Examples of problems that can be modeled as state space searches are also provided, like the vacuum world, 8-puzzle, and robotic assembly problems.

CptS 440 / 540 Artificial Intelligence

The document discusses search techniques in artificial intelligence. It defines search as finding a sequence of actions to achieve a goal state. Common problems that use search include problem solving, natural language processing, computer vision, and machine learning. Search involves defining a search space with states, operators to transition between states, an initial state, and a goal test. Popular uninformed search techniques like breadth-first search and depth-first search are explained. The document also introduces informed search techniques like uniform cost search that use cost information to guide the search towards optimal solutions.

Searching

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.

state-spaces29Sep06.ppt

The document discusses problem solving through search. It defines intelligent agents, search problems, and search graphs. Search problems are formulated using states, operators, start states, and goal states. Several search algorithms are introduced, including depth-first search and breadth-first search. Examples of search problems discussed include finding a route from Arad to Bucharest in Romania, the vacuum world problem, the 8-queens problem, and the 8-puzzle problem. The document outlines how to represent these problems as state spaces and formulates them in terms of states, actions, initial states, and goal tests. It also introduces tree search algorithms and strategies for searching state spaces, such as uninformed blind search and informed heuristic search.

Search 1

The document discusses various search algorithms used in artificial intelligence problem solving including breadth-first search, uniform-cost search, depth-first search, iterative deepening depth-first search, and bidirectional search. It provides examples of route finding problems and defines the components of a search problem. It also analyzes and compares the algorithms based on their completeness, time complexity, space complexity, and ability to find optimal solutions.

Chapter 3.pptx

The document summarizes different search strategies for solving problems by searching state spaces, including uninformed strategies like breadth-first search, depth-first search, and iterative deepening search, as well as informed strategies like greedy best-first search and A* search. Breadth-first search expands the shallowest nodes first and is complete and optimal for uniform costs. Depth-first search expands the deepest nodes first but may not terminate. Iterative deepening search runs depth-first search iteratively with increasing depth limits to guarantee completeness. Greedy best-first search uses a heuristic to select nodes but can get stuck in loops. A* search uses both path cost and heuristic cost and is guaranteed to find the optimal solution if the heuristic

Informed search (heuristics)

1) The document discusses various search algorithms including uninformed searches like breadth-first search as well as informed searches using heuristics.
2) It describes greedy best-first search which uses a heuristic function to select the node closest to the goal at each step, and A* search which uses both path cost and heuristic cost to guide the search.
3) Genetic algorithms are introduced as a search technique that generates successors by combining two parent states through crossover and mutation rather than expanding single nodes.

c4.pptx

Artificial intelligence techniques can be used to solve search problems by modeling them as trees. Common search strategies include breadth-first search, depth-first search, uniform cost search, and iterative deepening search. These strategies differ in terms of completeness, optimality, time complexity, and space complexity. More advanced techniques like bidirectional search can improve search efficiency by exploring the problem space from both the initial and goal states simultaneously.

PPT ON INTRODUCTION TO AI- UNIT-1-PART-2.pptx

The document discusses different types of agents and problem solving by searching. It describes four types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. It also covers formulating problems, searching strategies, problem solving by searching, measuring performance of searches, types of search strategies including uninformed and informed searches, and specific search algorithms like breadth-first search, uniform cost search, depth-first search, and depth-limited search.

l2.pptx

This document discusses search techniques used in artificial intelligence problems. It defines key concepts related to search spaces such as states, actions, goals, and costs. It provides examples of search problems in domains like the 8-puzzle, robot assembly, and missionaries and cannibals. It analyzes search algorithms like breadth-first search, depth-first search, uniform cost search, iterative deepening search, and informed searches using heuristics. The document compares the properties of different search strategies.

Lecture 3 Problem Solving.pptx

The document discusses various search algorithms used in artificial intelligence problem solving. It defines key search terminology like problem space, states, actions, and goals. It then explains different types of search problems and provides examples like the 8-puzzle and vacuuming world problems. Finally, it summarizes uninformed search strategies like breadth-first search, depth-first search, and iterative deepening search as well as informed strategies like greedy best-first search and A* search which use heuristics to guide the search.

l2.pptx

This document discusses search algorithms in artificial intelligence. It describes how search is used to find solutions in many AI problems by exploring possible states. The key aspects covered are:
- Search involves exploring a space of possible states through actions or operators to find a goal state.
- Problems are formulated as a search problem defined by the initial state, operators/actions, goal test, and cost function.
- Common uninformed search algorithms like breadth-first search (BFS) and depth-first search (DFS) are described and their properties analyzed.
- BFS is complete but can require exponential time and space, while DFS is not complete but uses less space in most cases.
- Uniform

chapter3part1.ppt

This document summarizes various search algorithms and toy problems that are used to illustrate problem solving by searching. It begins by introducing problem solving as finding a sequence of actions to achieve a goal from an initial state. It then discusses uninformed search strategies like breadth-first, depth-first, and uniform cost search. Several toy problems are presented, including the 8-puzzle, vacuum world, missionaries and cannibals problem. Real-world problems involving route finding, VLSI layout, and robot navigation are also briefly described. Evaluation criteria for search algorithms like space/time complexity and optimality/completeness are covered. Finally, iterative deepening search is introduced as a way to overcome depth limitations.

Informed-search TECHNIQUES IN ai ml data science

Informed search algorithms use problem-specific heuristics to improve search efficiency over uninformed methods. The most common informed methods are best-first search, A* search, and memory-bounded variants like RBFS and SMA*. A* is optimal if the heuristic is admissible for tree searches or consistent for graph searches. Heuristics provide an estimate of the remaining cost to the goal and can significantly speed up search. Common techniques for generating heuristics include Manhattan distance.

Dfs presentation

The document describes depth-first search (DFS), an algorithm for traversing or searching trees or graphs. It defines DFS, explains the process as visiting nodes by going deeper until reaching the end and then backtracking, provides pseudocode for the algorithm, gives an example on a directed graph, and discusses time complexity (O(V+E)), advantages like linear memory usage, and disadvantages like possible infinite traversal without a cutoff depth.

uniformed (also called blind search algo)

The document discusses various uninformed (blind) search strategies: breadth-first search, uniform-cost search, depth-first search, and iterative deepening search. It provides information on the properties of each strategy, including completeness, time complexity, space complexity, and optimality. Iterative deepening search is generally preferred for uninformed searches as it has linear space complexity like depth-first search but is complete unlike depth-first search. Bidirectional search is preferred when applicable as it can reduce the search space in half.

2012wq171-03-UninformedSeknlk ;lm,l;mk;arch.ppt

The document discusses various uninformed (blind) search strategies: breadth-first search, uniform-cost search, depth-first search, and iterative deepening search. It provides information on the properties of each strategy, including completeness, time and space complexity, and optimality. Iterative deepening search is generally preferred for uninformed searches as it has linear space complexity like depth-first search but is complete unlike depth-first search. Bidirectional search is preferred when applicable as it can reduce the search space in half.

Searchadditional2

The document discusses various informed search strategies including best-first search, greedy best-first search, A* search, and local search algorithms like hill-climbing search and simulated annealing search. It explains how heuristic functions can help guide search toward solutions more efficiently. Key aspects covered are how A* search uses an evaluation function f(n) = g(n) + h(n) to expand the most promising nodes first, and how hill-climbing search gets stuck at local optima but simulated annealing incorporates randomness to help escape them.

AIw09.pptx

Online search is useful when there is no environment transition model available and the environment is nondeterministic. It involves the agent taking actions in the environment without knowing the results in advance. Two common online search algorithms are online depth-first search and Learning Real-Time A* (LRTA*), which estimates costs to guide the search. Constraint satisfaction problems can also be modeled and solved as online searches over factored state spaces using constraint propagation techniques like node and arc consistency to reduce the search space.

Searching techniques

Searching is a technique used in AI to solve problems by exploring possible states or solutions. The document discusses various search algorithms used in single-agent pathfinding problems like sliding tile puzzles. It describes brute force search strategies like breadth-first search and depth-first search, and informed search strategies like A* search, greedy best-first search, hill-climbing search and simulated annealing that use heuristic functions. Local search algorithms are also summarized.

Ai popular search algorithms

This presentation educates you about AI - Popular Search Algorithms, Single Agent Pathfinding Problems, Search Terminology, Brute-Force Search Strategies, Breadth-First Search and Depth-First Search with example chart.
For more topics stay tuned with Learnbay.

Shive

This document summarizes various algorithms for robot navigation in discrete and continuous environments. It first discusses uninformed search algorithms like depth-first search (DFS) and breadth-first search (BFS). It then covers informed search algorithms such as recursive best-first search (RBFS) and A* search. Other algorithms mentioned include genetic algorithms, hill climbing, ant colony optimization, and rapidly-exploring random trees for continuous environments. References are provided at the end for further reading.

Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

The document discusses problem formulation for solving problems using search algorithms. It provides examples of formulating problems like route finding between cities and solving the 8-puzzle as state space problems. Key components of problem formulation are defined as the initial state, successor function, goal test, and path cost. Real-life applications that can be formulated as search problems are also presented, such as robot navigation, vehicle routing, and assembly sequencing.

Jarrar.lecture notes.aai.2011s.ch4.informedsearch

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.

Lecture 3 problem solving

This document discusses problem solving by searching. It defines the key components of well-defined problems including the initial state, actions, transition model, goal test, and path cost. It provides examples of problems that can be formulated as searches, such as the 8-puzzle, route finding, and the traveling salesperson problem. It then covers different search strategies including uninformed searches like breadth-first, depth-first, and iterative deepening as well as informed searches like greedy best-first and A* that use heuristics to guide the search.

AI: AI & Problem Solving

The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.

AI: AI & problem solving

The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.

Lecture 16 - Dijkstra's Algorithm.pdf

For the family tree data structure, I would recommend using a graph represented by an adjacency list. This allows easy traversal of the connections between ancestors and descendants.
For the algorithm, I would recommend Dijkstra's algorithm. Dijkstra's finds the shortest path from a starting node to all other nodes in a weighted graph. We can assign each generation a "weight" of 1, so it finds the closest living descendant. It's efficient, running in O(VlogV+E) time which should be fast enough for a family tree. Using Dijkstra's takes advantage of the graph representation and efficiently solves the problem of finding the closest living relative.

ROBOETHICS-CCS345 ETHICS AND ARTIFICIAL INTELLIGENCE.ppt

This document summarizes the key topics discussed at the Friedrich-Ebert-Foundation - University of Tsukuba Joint Symposium on Robo-Ethics and „Mind-Body-Schema“ of Human and Robot: Challenges for a Better Quality of Life. The symposium addressed questions around the increasing prevalence of robots in society, how robots may impact humans, ethics of human-robot interaction, and ensuring robots are designed and used to benefit rather than replace humans.

Ethics in AI and Its imoact on Society etc

This document discusses three key aspects of ethics in artificial intelligence: treating AI systems ethically, ensuring ethics are built into AI machines, and using AI technologies ethically. It notes that as AI systems gain more human-like thinking abilities, they may require rights if able to experience pleasure and pain. Developers have a responsibility to design AI that can make choices and take actions autonomously in an ethical manner. However, some oppose giving AI ethics or allowing it to replace human roles like doctors and judges due to concerns about threats to human dignity.

PPT ON INTRODUCTION TO AI- UNIT-1-PART-2.pptx

The document discusses different types of agents and problem solving by searching. It describes four types of agent programs: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. It also covers formulating problems, searching strategies, problem solving by searching, measuring performance of searches, types of search strategies including uninformed and informed searches, and specific search algorithms like breadth-first search, uniform cost search, depth-first search, and depth-limited search.

l2.pptx

This document discusses search techniques used in artificial intelligence problems. It defines key concepts related to search spaces such as states, actions, goals, and costs. It provides examples of search problems in domains like the 8-puzzle, robot assembly, and missionaries and cannibals. It analyzes search algorithms like breadth-first search, depth-first search, uniform cost search, iterative deepening search, and informed searches using heuristics. The document compares the properties of different search strategies.

Lecture 3 Problem Solving.pptx

The document discusses various search algorithms used in artificial intelligence problem solving. It defines key search terminology like problem space, states, actions, and goals. It then explains different types of search problems and provides examples like the 8-puzzle and vacuuming world problems. Finally, it summarizes uninformed search strategies like breadth-first search, depth-first search, and iterative deepening search as well as informed strategies like greedy best-first search and A* search which use heuristics to guide the search.

l2.pptx

This document discusses search algorithms in artificial intelligence. It describes how search is used to find solutions in many AI problems by exploring possible states. The key aspects covered are:
- Search involves exploring a space of possible states through actions or operators to find a goal state.
- Problems are formulated as a search problem defined by the initial state, operators/actions, goal test, and cost function.
- Common uninformed search algorithms like breadth-first search (BFS) and depth-first search (DFS) are described and their properties analyzed.
- BFS is complete but can require exponential time and space, while DFS is not complete but uses less space in most cases.
- Uniform

chapter3part1.ppt

This document summarizes various search algorithms and toy problems that are used to illustrate problem solving by searching. It begins by introducing problem solving as finding a sequence of actions to achieve a goal from an initial state. It then discusses uninformed search strategies like breadth-first, depth-first, and uniform cost search. Several toy problems are presented, including the 8-puzzle, vacuum world, missionaries and cannibals problem. Real-world problems involving route finding, VLSI layout, and robot navigation are also briefly described. Evaluation criteria for search algorithms like space/time complexity and optimality/completeness are covered. Finally, iterative deepening search is introduced as a way to overcome depth limitations.

Informed-search TECHNIQUES IN ai ml data science

Informed search algorithms use problem-specific heuristics to improve search efficiency over uninformed methods. The most common informed methods are best-first search, A* search, and memory-bounded variants like RBFS and SMA*. A* is optimal if the heuristic is admissible for tree searches or consistent for graph searches. Heuristics provide an estimate of the remaining cost to the goal and can significantly speed up search. Common techniques for generating heuristics include Manhattan distance.

Dfs presentation

The document describes depth-first search (DFS), an algorithm for traversing or searching trees or graphs. It defines DFS, explains the process as visiting nodes by going deeper until reaching the end and then backtracking, provides pseudocode for the algorithm, gives an example on a directed graph, and discusses time complexity (O(V+E)), advantages like linear memory usage, and disadvantages like possible infinite traversal without a cutoff depth.

uniformed (also called blind search algo)

The document discusses various uninformed (blind) search strategies: breadth-first search, uniform-cost search, depth-first search, and iterative deepening search. It provides information on the properties of each strategy, including completeness, time complexity, space complexity, and optimality. Iterative deepening search is generally preferred for uninformed searches as it has linear space complexity like depth-first search but is complete unlike depth-first search. Bidirectional search is preferred when applicable as it can reduce the search space in half.

2012wq171-03-UninformedSeknlk ;lm,l;mk;arch.ppt

The document discusses various uninformed (blind) search strategies: breadth-first search, uniform-cost search, depth-first search, and iterative deepening search. It provides information on the properties of each strategy, including completeness, time and space complexity, and optimality. Iterative deepening search is generally preferred for uninformed searches as it has linear space complexity like depth-first search but is complete unlike depth-first search. Bidirectional search is preferred when applicable as it can reduce the search space in half.

Searchadditional2

The document discusses various informed search strategies including best-first search, greedy best-first search, A* search, and local search algorithms like hill-climbing search and simulated annealing search. It explains how heuristic functions can help guide search toward solutions more efficiently. Key aspects covered are how A* search uses an evaluation function f(n) = g(n) + h(n) to expand the most promising nodes first, and how hill-climbing search gets stuck at local optima but simulated annealing incorporates randomness to help escape them.

AIw09.pptx

Online search is useful when there is no environment transition model available and the environment is nondeterministic. It involves the agent taking actions in the environment without knowing the results in advance. Two common online search algorithms are online depth-first search and Learning Real-Time A* (LRTA*), which estimates costs to guide the search. Constraint satisfaction problems can also be modeled and solved as online searches over factored state spaces using constraint propagation techniques like node and arc consistency to reduce the search space.

Searching techniques

Searching is a technique used in AI to solve problems by exploring possible states or solutions. The document discusses various search algorithms used in single-agent pathfinding problems like sliding tile puzzles. It describes brute force search strategies like breadth-first search and depth-first search, and informed search strategies like A* search, greedy best-first search, hill-climbing search and simulated annealing that use heuristic functions. Local search algorithms are also summarized.

Ai popular search algorithms

This presentation educates you about AI - Popular Search Algorithms, Single Agent Pathfinding Problems, Search Terminology, Brute-Force Search Strategies, Breadth-First Search and Depth-First Search with example chart.
For more topics stay tuned with Learnbay.

Shive

This document summarizes various algorithms for robot navigation in discrete and continuous environments. It first discusses uninformed search algorithms like depth-first search (DFS) and breadth-first search (BFS). It then covers informed search algorithms such as recursive best-first search (RBFS) and A* search. Other algorithms mentioned include genetic algorithms, hill climbing, ant colony optimization, and rapidly-exploring random trees for continuous environments. References are provided at the end for further reading.

Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

The document discusses problem formulation for solving problems using search algorithms. It provides examples of formulating problems like route finding between cities and solving the 8-puzzle as state space problems. Key components of problem formulation are defined as the initial state, successor function, goal test, and path cost. Real-life applications that can be formulated as search problems are also presented, such as robot navigation, vehicle routing, and assembly sequencing.

Jarrar.lecture notes.aai.2011s.ch4.informedsearch

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.

Lecture 3 problem solving

This document discusses problem solving by searching. It defines the key components of well-defined problems including the initial state, actions, transition model, goal test, and path cost. It provides examples of problems that can be formulated as searches, such as the 8-puzzle, route finding, and the traveling salesperson problem. It then covers different search strategies including uninformed searches like breadth-first, depth-first, and iterative deepening as well as informed searches like greedy best-first and A* that use heuristics to guide the search.

AI: AI & Problem Solving

The document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.

AI: AI & problem solvingThe document discusses various problem solving techniques in artificial intelligence, including different types of problems, components of well-defined problems, measuring problem solving performance, and different search strategies. It describes single-state and multiple-state problems, and defines the key components of a problem including the data type, operators, goal test, and path cost. It also explains different search strategies such as breadth-first search, uniform cost search, depth-first search, depth-limited search, iterative deepening search, and bidirectional search.

Lecture 16 - Dijkstra's Algorithm.pdf

For the family tree data structure, I would recommend using a graph represented by an adjacency list. This allows easy traversal of the connections between ancestors and descendants.
For the algorithm, I would recommend Dijkstra's algorithm. Dijkstra's finds the shortest path from a starting node to all other nodes in a weighted graph. We can assign each generation a "weight" of 1, so it finds the closest living descendant. It's efficient, running in O(VlogV+E) time which should be fast enough for a family tree. Using Dijkstra's takes advantage of the graph representation and efficiently solves the problem of finding the closest living relative.

PPT ON INTRODUCTION TO AI- UNIT-1-PART-2.pptx

PPT ON INTRODUCTION TO AI- UNIT-1-PART-2.pptx

l2.pptx

l2.pptx

Lecture 3 Problem Solving.pptx

Lecture 3 Problem Solving.pptx

l2.pptx

l2.pptx

chapter3part1.ppt

chapter3part1.ppt

Informed-search TECHNIQUES IN ai ml data science

Informed-search TECHNIQUES IN ai ml data science

Dfs presentation

Dfs presentation

uniformed (also called blind search algo)

uniformed (also called blind search algo)

2012wq171-03-UninformedSeknlk ;lm,l;mk;arch.ppt

2012wq171-03-UninformedSeknlk ;lm,l;mk;arch.ppt

Searchadditional2

Searchadditional2

AIw09.pptx

AIw09.pptx

Searching techniques

Searching techniques

Ai popular search algorithms

Ai popular search algorithms

Shive

Shive

Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch

Jarrar.lecture notes.aai.2011s.ch4.informedsearch

Jarrar.lecture notes.aai.2011s.ch4.informedsearch

Lecture 3 problem solving

Lecture 3 problem solving

AI: AI & Problem Solving

AI: AI & Problem Solving

AI: AI & problem solving

AI: AI & problem solving

Lecture 16 - Dijkstra's Algorithm.pdf

Lecture 16 - Dijkstra's Algorithm.pdf

ROBOETHICS-CCS345 ETHICS AND ARTIFICIAL INTELLIGENCE.ppt

This document summarizes the key topics discussed at the Friedrich-Ebert-Foundation - University of Tsukuba Joint Symposium on Robo-Ethics and „Mind-Body-Schema“ of Human and Robot: Challenges for a Better Quality of Life. The symposium addressed questions around the increasing prevalence of robots in society, how robots may impact humans, ethics of human-robot interaction, and ensuring robots are designed and used to benefit rather than replace humans.

Ethics in AI and Its imoact on Society etc

This document discusses three key aspects of ethics in artificial intelligence: treating AI systems ethically, ensuring ethics are built into AI machines, and using AI technologies ethically. It notes that as AI systems gain more human-like thinking abilities, they may require rights if able to experience pleasure and pain. Developers have a responsibility to design AI that can make choices and take actions autonomously in an ethical manner. However, some oppose giving AI ethics or allowing it to replace human roles like doctors and judges due to concerns about threats to human dignity.

Machine Learning for AIML course UG.pptx

Machine learning builds prediction models by learning from previous data to predict the output of new data. It uses large amounts of data to build accurate models that improve automatically over time without being explicitly programmed. Machine learning detects patterns in data through supervised learning using labeled training data, unsupervised learning on unlabeled data to group similar objects, or reinforcement learning where an agent receives rewards or penalties to learn from feedback. It is widely used for problems like decision making, data mining, and finding hidden patterns.

Feature Engineering for data science.pptx

Feature engineering is a machine learning technique that uses data to create new variables not present in the training set. It involves techniques like outlier detection and removal, one hot encoding, log transforms, dimensionality reduction using PCA, handling missing values, and scaling. Outliers are unusual data points that differ significantly from other samples and can occur due to errors or be legitimate variations, while percentiles describe the value a given percentage of values are lower than.

NumPy_Broadcasting Data Science - Python.pptx

NumPy arrays can be broadcast together to perform arithmetic operations even if they have different shapes. Broadcasting duplicates smaller arrays to match the shape of larger arrays. It allows arrays with incompatible shapes to still be used together in arithmetic operations. This technique greatly simplifies code. Boolean arrays can be used to select, count, or modify values in NumPy arrays based on logical criteria using techniques like masking and fancy indexing.

NumPy_Aggregations - Python for Data Science.pptx

This document discusses using NumPy to perform aggregations and calculations on arrays. It shows how NumPy's sum, min, max and other aggregation functions provide significant performance improvements over native Python functions. It also demonstrates calculating statistics like mean, standard deviation, percentiles on a dataset of US President heights to find the average height.

R Factor.pptx

Zero Waste is a philosophy that encourages redesigning resource life cycles so that all products are reused and no trash is sent to landfills. It is a whole systems approach aiming for massive change in how materials flow through society with no waste. Zero waste refers to waste management planning that emphasizes waste prevention over end-of-pipe management. It supports ecological, human and economic health through sustainable practices.

Research Techniques.ppt

This document discusses key concepts related to scientific research methods and hypothesis formulation. It covers the basic elements of the scientific method including empiricism, determinism, and skepticism. The stages of the scientific method are outlined, including choosing a question, identifying a hypothesis, making testable predictions, designing an experiment, collecting and analyzing data, and determining if results support the hypothesis. Descriptive study designs like case series and population surveys are discussed as ways to help formulate hypotheses by examining person, place, and time factors. Guidelines are provided for properly framing hypotheses in research studies.

Research.ppt

This course introduces research methods and is designed to achieve several goals: introduce research methods concepts, basic biostatistics, biostatistics software, the research process, and developing a research project. The main assignment is for students to develop their own research project by identifying a research problem, formulating research questions and hypotheses, and selecting an appropriate research design. The document provides guidance on developing good research questions and hypotheses, and identifies the key components of a research question as the variables, population, and testability. It also distinguishes between directional and non-directional hypotheses. Students will present their research projects during the last day of class.

ROBOETHICS-CCS345 ETHICS AND ARTIFICIAL INTELLIGENCE.ppt

ROBOETHICS-CCS345 ETHICS AND ARTIFICIAL INTELLIGENCE.ppt

Ethics in AI and Its imoact on Society etc

Ethics in AI and Its imoact on Society etc

Machine Learning for AIML course UG.pptx

Machine Learning for AIML course UG.pptx

Feature Engineering for data science.pptx

Feature Engineering for data science.pptx

NumPy_Broadcasting Data Science - Python.pptx

NumPy_Broadcasting Data Science - Python.pptx

NumPy_Aggregations - Python for Data Science.pptx

NumPy_Aggregations - Python for Data Science.pptx

R Factor.pptx

R Factor.pptx

Research Techniques.ppt

Research Techniques.ppt

Research.ppt

Research.ppt

bank management system in java and mysql report1.pdf

truth is high but higher still is truth full living

A review on techniques and modelling methodologies used for checking electrom...

The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.

New techniques for characterising damage in rock slopes.pdf

rock mass characterization and New techniques for characterising damage in rock slopes

哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样

原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf

Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...

CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT

The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.

Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf

Iron and Steel Technology towards Sustainable Steelmaking

International Conference on NLP, Artificial Intelligence, Machine Learning an...

International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor

Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia22CYT12-Unit-V-E Waste and its Management.ppt

Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.

6th International Conference on Machine Learning & Applications (CMLA 2024)

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.

BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf

Guia para el codigo ASME

Series of visio cisco devices Cisco_Icons.ppt

To be used on Visio

Advanced control scheme of doubly fed induction generator for wind turbine us...

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.

A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS

The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

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.

bank management system in java and mysql report1.pdf

bank management system in java and mysql report1.pdf

A review on techniques and modelling methodologies used for checking electrom...

A review on techniques and modelling methodologies used for checking electrom...

New techniques for characterising damage in rock slopes.pdf

New techniques for characterising damage in rock slopes.pdf

哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样

哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样

Manufacturing Process of molasses based distillery ppt.pptx

Manufacturing Process of molasses based distillery ppt.pptx

[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf

[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf

spirit beverages ppt without graphics.pptx

spirit beverages ppt without graphics.pptx

CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT

CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT

Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf

Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf

International Conference on NLP, Artificial Intelligence, Machine Learning an...

International Conference on NLP, Artificial Intelligence, Machine Learning an...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...

22CYT12-Unit-V-E Waste and its Management.ppt

22CYT12-Unit-V-E Waste and its Management.ppt

6th International Conference on Machine Learning & Applications (CMLA 2024)

6th International Conference on Machine Learning & Applications (CMLA 2024)

BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf

BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf

Series of visio cisco devices Cisco_Icons.ppt

Series of visio cisco devices Cisco_Icons.ppt

Advanced control scheme of doubly fed induction generator for wind turbine us...

Advanced control scheme of doubly fed induction generator for wind turbine us...

A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS

A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS

Properties Railway Sleepers and Test.pptx

Properties Railway Sleepers and Test.pptx

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions

- 1. Problem Solving Agents Blind and Informed Searches
- 3. Problem Types Well defined Initial state Operator(Successor and predecessor Functions) Goal Test Path cost function Non well defined Missing at least on criteria
- 4. Example 1: TSM In Romania On holiday in Romania; currently in Arad. Formulate goal: be in Bucharest Formulate problem: states: various cities actions: drive between cities Find solution: sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest Goal Test> Are we in Bucharest. Cost Function> Sum of road lengths to the destination
- 7. Example 2: 8-Puzzel states??: integer locations of tiles (ignore intermediate positions) actions??: move blank left, right, up, down (ignore unjamming etc.) transition model??: effect of the actions goal test??: = goal state (given) path cost??: 1 per move [Note: optimal solution of n-Puzzle family is NP-hard]
- 8. Example Problems Toy problems vacuum cleaner agent 8-puzzle 8-queens Crypt arithmetic missionaries cannibals Real-world problems route finding traveling salesperson VLSI layout robot navigation assembly sequencing
- 9. Search Know the fundamental search strategies and algorithms uninformed search breadth-first, depth-first, uniform-cost, iterative deepening, bidirectional informed search best-first (greedy, A*), heuristics, memory-bounded Evaluate the suitability of a search strategy for a problem completeness, optimality, time & space complexity
- 10. Searching for Solutions Traversal of some search space from the initial state to a goal state legal sequence of actions as defined by operators The search can be performed on On a search tree derived from expanding the current state using the possible operators Tree-Search algorithm A graph representing the state space Graph-Search algorithm
- 12. Uninformed search strategies Uninformed strategies use only the information available in the problem definition Breadth-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening search Bidirectional Search
- 13. BFS Expand shallowest unexpanded node (shortest path in the frontier)
- 14. Evaluation
- 15. Evaluation Complete?? Yes (if b is finite) Optimal?? Yes (if cost = 1 per step); not optimal in general Time?? b^d Number of nodes generated: 1 + b + b^2 + … + b^d Space?? b^d Space is the big problem; can easily generate nodes at 100MB/sec so 24hrs = 8640GB.
- 16. Uniform-cost search Expand first least-cost path (Equivalent to breadth-first if step costs all equal) Implementation: fringe = priority queue ordered by path cost, lowest first
- 18. DFS Depth first search is another way of traversing graphs, which is closely related to preorder traversal of a tree. Recall that preorder traversal simply visits each node before its children. It is most easy to program as a recursive routine. Complete?? No Optimal?? No Time?? b^d Space?? b*d
- 19. Depth limited search A version of DFS in which l is defined by an expert There is a chance of converging to local optima Complete?? Yes(if l>d) Optimal?? Yes Time?? b^l Space?? b*l
- 20. Iterative Deeping Search(IDS) This search strategy always expands one node to the deepest level of the tree. Only when a dead-end is encountered does the search backup and expand nodes at shallower levels. N=(d+1)1+(d)b+(d-1)b2+...+(3)bd-2+(2)bd-1+bd
- 21. IDS Evaluation Complete?? Yes Optimal?? Yes Time?? b^d Space?? b.d
- 22. Bidirectional search Idea: Run two simultaneous searches. One Forward from initial state One Backward from goal state Until two fingers met
- 23. Summary