I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III. BEST FIRST SEARCH IN AI
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The document discusses different types of search algorithms in artificial intelligence. It describes informed search which uses heuristics and knowledge to find solutions more efficiently compared to uninformed searches. It provides details on heuristic functions which estimate how close a state is to the goal. The document also explains best first search, an informed search technique that uses heuristic values and a priority queue to iteratively explore the most promising nodes first in searching for a solution.
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I.BEST FIRST SEARCH IN AI
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
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Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
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Informed search
The document discusses informed search techniques that use heuristic information to guide the search for a solution more efficiently. It describes how heuristic information about the problem domain can help constrain the search space. Hill climbing and best-first search are two informed search strategies discussed. Hill climbing iteratively moves to successor states with improved heuristic values until a local optimum is reached. Best-first search maintains an open list of promising nodes to explore and prioritizes expanding nodes with the best heuristic values to avoid getting stuck in local optima.
A* Algorithm
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
Hill climbing
This document discusses various heuristic search algorithms including generate-and-test, hill climbing, best-first search, problem reduction, and constraint satisfaction. Generate-and-test involves generating possible solutions and testing if they are correct. Hill climbing involves moving in the direction that improves the state based on a heuristic evaluation function. Best-first search evaluates nodes and expands the most promising node first. Problem reduction breaks problems into subproblems. Constraint satisfaction views problems as sets of constraints and aims to constrain the problem space as much as possible.
02 problem solving_search_control
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
Artificial Intelligence Searching Techniques
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
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I. Hill climbing algorithm II. Steepest hill climbing algorithm
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
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Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
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, Recursive Best First Search, Pruning the CLOSED and OPEN
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I.BEST FIRST SEARCH IN AI
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
I. Mini-Max Algorithm in AI
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
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Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
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Informed search
The document discusses informed search techniques that use heuristic information to guide the search for a solution more efficiently. It describes how heuristic information about the problem domain can help constrain the search space. Hill climbing and best-first search are two informed search strategies discussed. Hill climbing iteratively moves to successor states with improved heuristic values until a local optimum is reached. Best-first search maintains an open list of promising nodes to explore and prioritizes expanding nodes with the best heuristic values to avoid getting stuck in local optima.
A* Algorithm
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
Hill climbing
This document discusses various heuristic search algorithms including generate-and-test, hill climbing, best-first search, problem reduction, and constraint satisfaction. Generate-and-test involves generating possible solutions and testing if they are correct. Hill climbing involves moving in the direction that improves the state based on a heuristic evaluation function. Best-first search evaluates nodes and expands the most promising node first. Problem reduction breaks problems into subproblems. Constraint satisfaction views problems as sets of constraints and aims to constrain the problem space as much as possible.
02 problem solving_search_control
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
Artificial Intelligence Searching Techniques
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
Heuristc Search Techniques
This document discusses various heuristic search techniques, including generate-and-test, hill climbing, best first search, and simulated annealing. Generate-and-test involves generating possible solutions and testing them until a solution is found. Hill climbing iteratively improves the current state by moving in the direction of increased heuristic value until no better state can be found or a goal is reached. Best first search expands the most promising node first based on heuristic evaluation. Simulated annealing is based on hill climbing but allows moves to worse states probabilistically to escape local maxima.
Heuristic search-in-artificial-intelligence
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Hill climbing algorithm in artificial intelligence
The hill climbing algorithm is a local search technique used to find the optimal solution to a problem. It works by starting with an initial solution and iteratively moving to a neighboring solution that has improved value until no better solutions can be found. Simple hill climbing only considers one neighbor at a time, while steepest ascent examines all neighbors and chooses the one closest to the optimal solution. The algorithm can get stuck at local optima rather than finding the global optimum. Techniques like simulated annealing incorporate randomness to help escape local optima.
A Star Search
The document discusses the A* search algorithm, which is an informed search or heuristic search algorithm. A* combines the best aspects of uniform cost search and greedy best-first search. It is guaranteed to find the shortest path to the goal, if such a path exists. A* evaluates nodes by using both the path cost from the start node to the current node, plus an estimate of the cost to get from the current node to the goal node. It prioritizes expanding the most promising nodes first, those with the lowest combined cost. A* is optimal and admissible if the heuristic function never overestimates the actual cost to the goal.
Heuristics Search Techniques in AI
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
search strategies in artificial intelligence
The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.
AI_Session 7 Greedy Best first search algorithm.pptx
This document summarizes key topics from a session on problem solving by search algorithms in artificial intelligence. It discusses uninformed search strategies like breadth-first search and depth-first search. It also covers informed, heuristic search strategies such as greedy best-first search and A* search which use heuristic functions to estimate distance to the goal. Examples are provided to illustrate best first search, and it describes how this algorithm expands nodes and uses priority queues to order nodes by estimated cost. The next session is slated to cover the A* search algorithm in more detail.
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This document summarizes a session on problem solving by search in artificial intelligence. It discusses uninformed and informed search strategies like breadth-first search, uniform cost search, depth-first search, greedy best-first search, and A* search. It also covers searching with non-deterministic actions, partial observations, and online search agents operating in unknown environments. Examples discussed include the vacuum world problem and how search trees are used to handle non-determinism through contingency planning. The next session will cover online search agents operating in unknown environments.I. Alpha-Beta Pruning in ai
Alpha-beta pruning is a modification of the minimax algorithm that optimizes it by pruning portions of the search tree that cannot affect the outcome. It uses two thresholds, alpha and beta, to track the best values found for the maximizing and minimizing players. By comparing alpha and beta at each node, it can avoid exploring subtrees where the minimum of the maximizing player's options will be greater than the maximum of the minimizing player's options. This allows it to often prune branches of the tree without calculating their values, improving the algorithm's efficiency.
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Alpha beta
Alpha-beta pruning is an optimization technique for minimax algorithms that reduces computation time by pruning branches that are provably non-optimal. It works by maintaining alpha and beta values that represent the best values found for the maximizing and minimizing players. Branches can be pruned when alpha is greater than or equal to beta, without affecting the optimal outcome. Alpha-beta pruning allows searching much deeper levels of the game tree in the same amount of time as minimax.
Local search algorithm
Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...
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
BackTracking Algorithm: Technique and Examples
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
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.
Problem solving agents
This document discusses problem solving agents in artificial intelligence. It explains that problem solving agents focus on satisfying goals by formulating the goal based on the current situation, then formulating the problem by determining the actions needed to achieve the goal. Key components of problem formulation include the initial state, possible actions, transition model describing how actions change the state, a goal test, and path cost function. Two examples of well-defined problems are given: the 8-puzzle problem and the 8-queens problem.
A* Search Algorithm
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
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.
Ai notes
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Search problems in Artificial Intelligence
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
I.ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS) II.INFORMED SEARCH IN ARTIFI...
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
Informed search (bst)
The document discusses informed search, which applies heuristics to make search algorithms more efficient. A heuristic is a method that estimates the best solution in reasonable time rather than finding the exact best solution. Informed search techniques like best first search use a heuristic function to assign each node a value estimating its distance from the goal. The search prioritizes exploring nodes with lower heuristic values first in the open list in order to find the goal state faster.
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Heuristc Search Techniques
This document discusses various heuristic search techniques, including generate-and-test, hill climbing, best first search, and simulated annealing. Generate-and-test involves generating possible solutions and testing them until a solution is found. Hill climbing iteratively improves the current state by moving in the direction of increased heuristic value until no better state can be found or a goal is reached. Best first search expands the most promising node first based on heuristic evaluation. Simulated annealing is based on hill climbing but allows moves to worse states probabilistically to escape local maxima.
Heuristic search-in-artificial-intelligence
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Hill climbing algorithm in artificial intelligence
The hill climbing algorithm is a local search technique used to find the optimal solution to a problem. It works by starting with an initial solution and iteratively moving to a neighboring solution that has improved value until no better solutions can be found. Simple hill climbing only considers one neighbor at a time, while steepest ascent examines all neighbors and chooses the one closest to the optimal solution. The algorithm can get stuck at local optima rather than finding the global optimum. Techniques like simulated annealing incorporate randomness to help escape local optima.
A Star Search
The document discusses the A* search algorithm, which is an informed search or heuristic search algorithm. A* combines the best aspects of uniform cost search and greedy best-first search. It is guaranteed to find the shortest path to the goal, if such a path exists. A* evaluates nodes by using both the path cost from the start node to the current node, plus an estimate of the cost to get from the current node to the goal node. It prioritizes expanding the most promising nodes first, those with the lowest combined cost. A* is optimal and admissible if the heuristic function never overestimates the actual cost to the goal.
Heuristics Search Techniques in AI
What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
search strategies in artificial intelligence
The document discusses search strategies in artificial intelligence. It defines key terms like search space, start state, goal test, and search tree. It describes properties of search algorithms like completeness, optimality, time complexity, and space complexity. It differentiates between uninformed searches, which do not use domain knowledge, like breadth-first search and depth-first search, and informed searches, which use heuristics to guide the search more efficiently, like greedy search and A* search. The document outlines the differences between informed and uninformed searches.
AI_Session 7 Greedy Best first search algorithm.pptx
This document summarizes key topics from a session on problem solving by search algorithms in artificial intelligence. It discusses uninformed search strategies like breadth-first search and depth-first search. It also covers informed, heuristic search strategies such as greedy best-first search and A* search which use heuristic functions to estimate distance to the goal. Examples are provided to illustrate best first search, and it describes how this algorithm expands nodes and uses priority queues to order nodes by estimated cost. The next session is slated to cover the A* search algorithm in more detail.
AI_Session 11: searching with Non-Deterministic Actions and partial observati...
AI_Session 11: searching with Non-Deterministic Actions and partial observati...Asst.prof M.Gokilavani
This document summarizes a session on problem solving by search in artificial intelligence. It discusses uninformed and informed search strategies like breadth-first search, uniform cost search, depth-first search, greedy best-first search, and A* search. It also covers searching with non-deterministic actions, partial observations, and online search agents operating in unknown environments. Examples discussed include the vacuum world problem and how search trees are used to handle non-determinism through contingency planning. The next session will cover online search agents operating in unknown environments.I. Alpha-Beta Pruning in ai
Alpha-beta pruning is a modification of the minimax algorithm that optimizes it by pruning portions of the search tree that cannot affect the outcome. It uses two thresholds, alpha and beta, to track the best values found for the maximizing and minimizing players. By comparing alpha and beta at each node, it can avoid exploring subtrees where the minimum of the maximizing player's options will be greater than the maximum of the minimizing player's options. This allows it to often prune branches of the tree without calculating their values, improving the algorithm's efficiency.
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Alpha beta
Alpha-beta pruning is an optimization technique for minimax algorithms that reduces computation time by pruning branches that are provably non-optimal. It works by maintaining alpha and beta values that represent the best values found for the maximizing and minimizing players. Branches can be pruned when alpha is greater than or equal to beta, without affecting the optimal outcome. Alpha-beta pruning allows searching much deeper levels of the game tree in the same amount of time as minimax.
Local search algorithm
Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...
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
BackTracking Algorithm: Technique and Examples
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
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.
Problem solving agents
This document discusses problem solving agents in artificial intelligence. It explains that problem solving agents focus on satisfying goals by formulating the goal based on the current situation, then formulating the problem by determining the actions needed to achieve the goal. Key components of problem formulation include the initial state, possible actions, transition model describing how actions change the state, a goal test, and path cost function. Two examples of well-defined problems are given: the 8-puzzle problem and the 8-queens problem.
A* Search Algorithm
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
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.
Ai notes
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Search problems in Artificial Intelligence
This presentation discusses the state space problem formulation and different search techniques to solve these. Techniques such as Breadth First, Depth First, Uniform Cost and A star algorithms are covered with examples. We also discuss where such techniques are useful and the limitations.
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Hill climbing algorithm in artificial intelligence
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Informed search (bst)
The document discusses informed search, which applies heuristics to make search algorithms more efficient. A heuristic is a method that estimates the best solution in reasonable time rather than finding the exact best solution. Informed search techniques like best first search use a heuristic function to assign each node a value estimating its distance from the goal. The search prioritizes exploring nodes with lower heuristic values first in the open list in order to find the goal state faster.
Heuristis search
Heuristic search is a type of guided search that uses heuristics to direct the search towards the goal. It is faster than uninformed searches as it uses heuristics to estimate the cost to reach the goal from each node. Common heuristic search algorithms include generate-and-test, hill climbing, and best-first search. Heuristic search works by having a heuristic function estimate the cost of reaching the goal for each node and selecting the path with the lowest estimated cost.
What is A * Search? What is Heuristic Search? What is Tree search Algorithm?
What is A * Search? What is Heuristic Search? What is Tree search Algorithm?
Moving from one place to another is a task that we humans do almost every day. We try to find the shortest path that enables us to reach our destinations faster and make the whole process of traveling as efficient as possible. In the old days, we would trial and error with the paths available and had to assume which path taken was shorter or longer.
What is a Search Algorithm?
Tree search Algorithm
Breadth-First Search
Depth-First Search
Bidirectional Search
Uniform Cost Search
Iterative Deepening Depth-First Search
Heuristic Search
Manhattan distance
Pure Heuristic Search
A * Search
Formula
A * Search Explanation
AI_Lecture2.pptx
1) Means-ends analysis is a problem-solving technique that breaks down goals into sub-goals and determines actions to achieve each sub-goal step-by-step to ultimately reach the end goal.
2) It works by first evaluating the current state, defining the target goal, and then splitting the target goal into sub-goals linked to executable actions.
3) The actions are applied to reduce the differences between the current and target states, tracking changes made until the end goal is achieved.
Ai1.pdf
The document discusses the Breadth-First Search (BFS) algorithm. It begins with an introduction to graph traversal and explains that BFS is a graph traversal technique that explores all nodes layer-by-layer from a starting node. It then provides an example of applying BFS to a binary tree, showing the steps of selecting a starting node, inserting its children into a queue, extracting nodes from the queue and inserting their children, and repeating until the queue is empty. The document concludes by listing some applications of BFS such as for web crawlers, GPS navigation, and finding the minimum spanning tree of an unweighted graph.
artifical intelligence final paper
This document compares three popular path planning algorithms: A*, greedy best first search, and jump point search. It implements the algorithms in MATLAB using grid-based maps with random start/goal points and static obstacles. The algorithms are evaluated based on computational complexity, time complexity, and space complexity. Jump point search generally has the best performance out of the three algorithms as it can make long jumps along straight lines in the grid, exploring fewer nodes than A*.
lecture 6 AI - A star.pdf
The document discusses various search algorithms such as best-first search, A* search, hill-climbing search, and forgetful hill-climbing search. It explains that A* search combines the cost so far with a heuristic estimate of the remaining cost to provide an optimal path. The heuristic must be admissible, meaning it cannot overestimate the cost to the goal. With an admissible heuristic, A* search is proven to always find an optimal solution.
09_Informed_Search.ppt
This document summarizes an AI lecture on informed search strategies. It discusses blind searches like depth-first and breadth-first that do not use domain knowledge. Informed searches like best-first, A*, and greedy search aim branches more intelligently using heuristics. The optimal A* algorithm sorts states by cost-so-far plus admissible heuristic estimates. An example 8-puzzle heuristic calculates Manhattan distances of tiles to positions. Comparing search costs shows more informed heuristics explore less of the problem space. Next week's exercise involves solving Missionaries and Cannibals with agenda-based search.
Unit1_AI&ML_leftover (2).pptx
Informed search algorithms use heuristics to more efficiently find goal nodes in large search spaces. Heuristics estimate how close a state is to the goal and help guide the search. The heuristic function must be admissible, meaning its estimated cost must be less than or equal to the actual cost. Bayes' theorem allows calculating conditional probabilities and is fundamental to probabilistic reasoning, which represents knowledge with uncertainty using probabilities. Fuzzy set theory introduces vagueness by assigning membership degrees between 0 and 1 to represent how well something belongs to a set, like how sunny a day is based on cloud cover.
ArtificialIntelligenceInObjectDetection-Report.pdf
This document provides a summary of recent developments in artificial intelligence for object detection. It discusses different architectures based on convolutional neural networks for tasks like face detection, object detection, and video object co-segmentation. Region-based models like R-CNN, Fast R-CNN, and Faster R-CNN are described along with single-shot detectors like YOLO. The document also discusses using fuzzy logic for 3D modeling and the author's own work on face and motion detection in Python. In conclusion, real-time object detection has advanced through these techniques but there remains room for improvement, especially addressing issues in models like YOLO.
CSA 2001 (Module-2).pptx
The document provides an overview of various search strategies and algorithms used in artificial intelligence problem solving, including:
- Uninformed searches like depth-first search (DFS) and breadth-first search (BFS) that do not use additional information about the problem space.
- Informed searches like greedy search, best-first search, A* search that use heuristics to guide the search towards an optimal solution.
- Specific algorithms are described for solving problems like fractional knapsack, job scheduling with deadlines, minimum spanning tree (Prim's and Kruskal's algorithms).
The key search strategies allow rational agents to systematically solve problems by traversing state spaces or graphs in an organized manner.
A Star Search
The document discusses the A* search algorithm, which is an informed search or heuristic search algorithm. A* combines the best aspects of uniform cost search and greedy best-first search. It is guaranteed to find the shortest path to the goal, if such a path exists. A* evaluates nodes by using both the path cost from the start node to the current node, plus an estimate of the cost to get from the current node to the goal node. It prioritizes expanding the most promising nodes first, those with the lowest combined cost. A* is optimal and admissible if the heuristic function never overestimates the actual cost to the goal.
Report_SmartSuggest
This document describes a solution called "Smart Suggest" that uses machine learning and big data techniques to help NYC taxi drivers choose pickups that will maximize their profits. K-means clustering is used to group pickup locations into zones. Factors like density, average tip, distance, and machine learning recommendations are then scored and weighted to calculate optimal pickup requests for drivers. The system was built using technologies like MapReduce, Hive, Mahout, MongoDB, and deployed as a Spring MVC web application with REST APIs.
AI unit-2 lecture notes.docx
Uninformed and informed search algorithms are used in artificial intelligence. Uninformed search methods like breadth-first search and depth-first search do not use additional problem knowledge to guide the search. Informed search methods like A* use a heuristic function to estimate the cost to reach the goal, guiding the search towards more promising solutions. The document provides details on various search algorithms, their time and space complexity, and how they traverse the problem space like using open and closed lists.
Heuristic Searching Algorithms Artificial Intelligence.pptx
The document discusses various heuristic search algorithms used in artificial intelligence including hill climbing, A*, best first search, and mini-max algorithms. It provides descriptions of each algorithm, including concepts, implementations, examples, and applications. Key points covered include how hill climbing searches for better states, how A* uses a cost function to find optimal paths, and how best first search uses a priority queue to search nodes in order of estimated cost to reach the goal.
Lecture 11 Informed Search
Heuristic search algorithms use heuristics, or problem-specific knowledge, to guide the search for a solution. Some heuristics guarantee completeness while others may sacrifice completeness to improve efficiency. A heuristic function estimates the cost to reach the goal state from the current state. For example, in the 8-puzzle problem the Manhattan distance heuristic estimates this cost as the sum of the distances each misplaced tile would need to move to reach its goal position. The example shows applying the Manhattan distance heuristic to guide the search for a solution to instances of the 8-puzzle problem.
AI_03_Solving Problems by Searching.pptx
This document provides a summary of Lecture 3 on problem-solving by searching. It describes how problem-solving agents can formulate goals and problems, represent the problem as a state space, and find solutions using search algorithms like breadth-first search, uniform-cost search, depth-first search, and iterative deepening search. Examples of search problems discussed include the Romania pathfinding problem, vacuum world, and the 8-puzzle.
AI3391 ARTIFICIAL INTELLIGENCE UNIT II notes.pdf
AI3391 ARTIFICIAL INTELLIGENCE
Department of Artificial Intelligence and Data Science under anna university syllabus
Real Estate Investment Advising Using Machine Learning
This document presents a comparative study of machine learning algorithms for real estate investment advising using property price prediction. It analyzes Linear Regression using gradient descent, K-Nearest Neighbors regression, and Random Forest regression on quarterly Mumbai real estate data from 2005-2016. Features like area, rooms, distance to landmarks, amenities are used to predict prices. Random Forest regression achieved the lowest errors in predicting testing data, making it the most feasible algorithm according to the study. The authors conclude it is a promising approach for real estate trend forecasting and developing an investment advising tool.
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I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III. BEST FIRST SEARCH IN AI
- 1. Topic To Be Covered: I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III. BEST FIRST SEARCH IN AI Jagdamba Education Society's SND College of Engineering & Research Centre Department of Computer Engineering SUBJECT: Artificial Intelligence & Robotics Lecture No-08 Prof.Dhakane Vikas N
- 2. Types of search in ai II. Informed Search In AI It is search with information. It is greedy search method Use knowledge to find steps to solution. Less Complexity(Time, Space) Quick Solution Know about Start state and Goal state & Also know how to reach. informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost, how to reach to goal node, etc. This knowledge help agents to explore less to the search space and find more efficiently the goal node. The informed search algorithm is more useful for large search space. Informed search algorithm uses the idea of heuristic, so it is also called Heuristic search.
- 3. HEURISTIC FUNCTION in ai Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time. This technique always use to find solution quickly.
- 4. HEURISTIC FUNCTION in ai Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time.
- 5. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value --------------------------------------------------------------------------- I. Euclidian Distance (Straight Line Distance Method) Euclidean distance is the distance between two points in Euclidean space. Euclidean space was originally devised by the Greek mathematician Euclid around 300 B.C.E. to study the relationships between angles and distances.
- 6. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value II. Manhattan Distance In case of 8-Puzzle problem Manhattan distances are nothing but Number of moves need to be made by AI agent so that it can reach to its goal state.
- 7. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value ------------------------------- III. No.of Misplaced Tiles
- 8. BEST FIRST SEARCH(BFS) This is informed search technique also called as HEURISTIC search. This algo. Works using heuristic value. This algorithm uses evaluation function to decide which adjacent node is most promising and then explore. Priority queue is used to store cost of function. Space &Time Complexity of BFS is also O(V+E) whereV is vertices and E is edges. Also Written as:-O(b) ^d Where, b->Branching factor d->depth
- 9. BEST FIRST SEARCH(BFS) Algorithm Priority queue ‘PQ’ containing initial states Loop If PQ=Empty Return Fail Else NODE<-Remove_First(PQ) If NODE=GOAL Return path from initial state to NODE ELSE Generate all successor of NODE and insert newly generated NODE into ‘PQ’ according to cost value. END LOOP
- 10. BEST FIRST SEARCH(BFS) Advantages of BFS: Memory efficient as compared with DFS & BFS It is Complete Disadvantages of BFS: It gives good solution but not optimal solution. In worst case it may behave like unguided DFS