The document discusses adversarial search and the minimax algorithm. It describes minimax as a way to find the optimal move in a two-player game by searching the game tree. In the tree, nodes representing the player's moves are called MAX nodes and aim to maximize value, while opponent nodes are called MIN nodes and aim to minimize value. The minimax algorithm searches the tree recursively to assign values at each node based on its children. An example of using minimax to search a tic-tac-toe game tree is provided to illustrate the algorithm.
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
Minmax Algorithm In Artificial Intelligence slidesSamiaAziz4
Mini-max algorithm is a recursive or backtracking algorithm that is used in decision-making and game theory. Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
This is a simple presentation on Game Theory in Network Security. I made it when I was searching for research points for my Master degree. Still searching for other research points. Any suggestions on research points in network security or network architecture? :)
This is a simple presentation on Game Theory in Network Security. I made it when I was searching for research points for my Master degree. Still searching for other research points. Any suggestions on research points in network security or network architecture? :)
Mastering Greedy Algorithms: Optimizing Solutions for Efficiency"22bcs058
Greedy algorithms are fundamental techniques used in computer science and optimization problems. They belong to a class of algorithms that make decisions based on the current best option without considering the overall future consequences. Despite their simplicity and intuitive appeal, greedy algorithms can provide efficient solutions to a wide range of problems across various domains.
At the core of greedy algorithms lies a simple principle: at each step, choose the locally optimal solution that seems best at the moment, with the hope that it will lead to a globally optimal solution. This principle makes greedy algorithms easy to understand and implement, as they typically involve iterating through a set of choices and making decisions based on some criteria.
One of the key characteristics of greedy algorithms is their greedy choice property, which states that at each step, the locally optimal choice leads to an optimal solution overall. This property allows greedy algorithms to make decisions without needing to backtrack or reconsider previous choices, resulting in efficient solutions for many problems.
Greedy algorithms are commonly used in problems involving optimization, scheduling, and combinatorial optimization. Examples include finding the minimum spanning tree in a graph (Prim's and Kruskal's algorithms), finding the shortest path in a weighted graph (Dijkstra's algorithm), and scheduling tasks to minimize completion time (interval scheduling).
Despite their effectiveness in many situations, greedy algorithms may not always produce the optimal solution for a given problem. In some cases, a greedy approach can lead to suboptimal solutions that are not globally optimal. This occurs when the greedy choice property does not guarantee an optimal solution at each step, or when there are conflicting objectives that cannot be resolved by a greedy strategy alone.
To mitigate these limitations, it is essential to carefully analyze the problem at hand and determine whether a greedy approach is appropriate. In some cases, greedy algorithms can be augmented with additional techniques or heuristics to improve their performance or guarantee optimality. Alternatively, other algorithmic paradigms such as dynamic programming or divide and conquer may be better suited for certain problems.
Overall, greedy algorithms offer a powerful and versatile tool for solving optimization problems efficiently. By understanding their principles and characteristics, programmers and researchers can leverage greedy algorithms to tackle a wide range of computational challenges and design elegant solutions that balance simplicity and effectiveness.
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
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
2. Introduction
• The minimax algorithm is a way of finding an optimal move in a two
player game. In the search tree for a two-player game, there are two kinds
of nodes, nodes representing yourmoves and nodes representing your
opponent's moves. Nodes representing your moves are generally drawn as
squares (or possibly upward pointing triangles):
• Below given symbols are also called MAX nodes. The goal at a MAX node
is to maximize the value of the subtree rooted at that node. To do this, a
MAX node chooses the child with the greatest value, and that becomes the
value of the MAX node.
2
3. • Below given are called MIN nodes. The goal at a MIN node is to minimize
the value of the subtree rooted at that node. To do this, a MIN node chooses
the child with the smallest value, and that becomes the value of the MIN
node.
3
5. Example: tic-tac-toe
• e (evaluation function → integer) = number of
available rows, columns, diagonals for MAX -
number of available rows, columns, diagonals
for MIN
• MAX plays with “X” and desires maximizing e.
• MIN plays with “0” and desires minimizing e.
• Symmetries are taken into account.
• A depth limit is used (2, in the example).
5
9. Example
• To demonstrate the minimax algorithm, I'm going to use the following tree.
Note that it's typical for two player games to have different branching
factors at each node. The move I make could make restrictions on what
moves are possible for the other player, or possibly remove restrictions.
Note also that in this example, we're ignoring what the game or the probem
space are in order to focus on the algorithm.
9
10. Example
• So now you've seen the whole search tree. For the rest of the diagrams, I'll
only show the portion of the tree that we've already explored at that
particular time. Thus, when we start the problem, all minimax sees is the
start node:
10
11. Example
• It begins like a depth first search, generating the first child. Then we see
this:
11
12. Example
• So far we've really seen no
evaluation values. The way
minimax works is to go down a
specified number of full moves
(where one "full move" is actually
a move by you and a move by your
opponent), then calculate
evaluation values for states at that
depth. For this example, we're
going to go down one full move,
which is one more level. When we
generate the values for those nodes,
here is what we see:
12
13. Example
• Now we have the values of the
children of the min node, so it
can do its work. It chooses the
minimum of the two child node
values, which is 3. Now we
have this:
13
14. Example
• The max node at the top still
has two other children nodes
that we need to generate and
search. We go on and generate
the second node and generate its
child. Since there is only one
child, the min node must take
it's value, and we have this:
14
15. Example
• Finally, minimax generates the
third child of the top-level max
node, generates its children, and
evaluates them:
15
16. Example
• Now the third min node chooses the
minimum of it's child node values, 1, and
we have this:
16
17. Example
• Finally we have all of the values of
the children of the max node at the
top level, so it chooses the
maximum of them, 15, and we get
the final solution:
17
18. Analysis
• The minimax algorithm performs a complete
depth-first exploration of the game tree.
• In minimax, at each point in the process, only
the nodes along a path of the tree are
considered and kept in memory.
18
19. Analysis
• If the maximum depth of the tree is m, and
there are b legal moves at each point, then
the time complexity is O(bm).
• The space complexity is:
– O(bm) for an algorithm that generates all
successors at once
– O(m) if it generates successors one at a time.
19