This document discusses adversarial search techniques used in artificial intelligence to model games as search problems. It introduces the minimax algorithm and alpha-beta pruning to determine optimal strategies by looking ahead in the game tree. These techniques allow computers to search deeper and play games like chess and Go at a world-champion level by evaluating board positions and pruning unfavorable branches in the search.
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
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
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
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
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
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
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
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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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.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
3. Adversarial search
Examine the problem that arise when we try to plan ahead in a world
where other agent are planning against us.
A good example is in board game.
Adversarial games, while much studies in AI, are a small part of game
theory in economics.
4. Typical AI assumptions
Two agents whose actions alternates.
Utility values for each agents are opposite to each other.
Creates the adversarial situation.
Fully observable environment.
In game theory terms: Zero-sum games of perfect information.
5. Search versus Games
Search – no adversary
Solution is (heuristic) method of finding goal.
Heuristic techniques can find the optimal solution.
Evaluation function: estimate of cost from start to goal through given node.
Example: path finding, scheduling activities.
Game – adversary
Solution is strategy (strategy specifies moves for every possible opponent reply.
Optimality depends on opponents.
Time limit force an approximation solution.
Evaluation function: evaluate “goodness” of game position.
Examples: Chess, Checkers, GO.
6. Game setup
Two player: MAX and MIN.
MAX moves first and they take turns until game is over.
Winner get awarded, looser get penalty.
Game as search:
Initial state: e.g. board configuration of the chess.
Successor function: list of( move, state) pairs specifying legal moves.
Terminal test: is the game finished?
Utility function: gives numerical values of terminal state. E.g. win(+1), lose(-1)
or draw(0) in tic-tac-toe or chess.
MAX uses search tree to determine the next move.
8. Minimax strategy: look ahead and reason
backward
Find the optimal strategy for MAX assuming an infallible MIN opponent.
Need to compute this all the down the tree.
Complete depth first exploration of the game tree.
Assumption: Both players play optimally.
Given a game tree, the optimal strategy can be determined by using the
minimax value of each node.
10. Practical problem with minimax search
Number of game state is exponential in the number of moves
Solution: do not examine every node.
=> pruning
Remove branches that do not influence the final decision.
11. Alpha-beta algorithm
Depth-first search only consider nodes along a single path at any time.
α= highest value choice that we can guarantee for MAX so far in the current
subtree.
β= lowest value choice that we can guarantee for MIN so far in the current
subtree.
Update value for α and β during search and prunes remaining branches as
soon as the value is known to be worse than the current α or β value for
MAX or MIN.
17. Effectiveness of Alpha-beta search
Worst-case
Branches are ordered so that no pruning takes place. In this case alpha-beta
gives no improvement over exhaustive search.
Best-case
Each player’s best move is the left-most alternative.
In practice, performance is closer to best rather than worst-case.
In practice, often get O(b(d/2)) rather than O(bd)
This is same as having branching factor of sqrt(b)
Since (sqrt(b))d =b(d/2)
i.e., we have effectively gone from b to square root of b
E.g., in chess go from b~35 to b~6
This permits much deeper search in the same amount of time.
Typically twice as deep.
18. Final comments about Alpha-beta
Pruning
Pruning does not affect the final result.
Entire subtree can be pruned.
Good move ordering improves effectiveness of pruning.
Repeated states are again possible.
Store them in memory = transposition table.
19. Practical implementation
How do we make these ideas practical in real life game trees?
Standard approach:
Cut-off test: (where do we stop descending the tree)
Depth limit
Better: iterative deepening
Cut-off only when no big changes are expected to occur next (quiescence search)
Evaluation function
When the search is cut-off, we evaluate the current state by estimating its utility using
an evaluation function.
20. Static (Heuristic) Evaluation Function
An evaluation function:
Estimate how good the current board configuration is for a player.
Typically, one figures out how it is for the player, and how good for the opponent, and
subtract the opponent score from the players.
Chess : value of white pieces – value of black pieces
Typical values from –infinity(loss) to +infinity(win) or[-1,+1].
If the board evaluation is X for a player, it is –X for opponent.
Many clever ideas about how to use the evaluation function.
E.g. null move heuristic: let opponent moves twice.
Examples
Chess
Checkers
Tic-tac-toe
21. Iterative (Progressive) Deepening
In real games, there is usually a time limit T on making a move.
How do we take this into account?
Using alpha-beta we cannot use “partial” result with any confidence unless the full
breadth of the tree has been searched.
So, we could be conservative and set a conservative depth-limit which guarantees that
we will find a move in time < T.
Disadvantage is that we may finish early, could do more search.
In practice, iterative deepening search (IDS) is used
IDS runs depth first search with an increasing depth limit.
When the clock runs out we use the solution found at the previous depth limit.
22. The state of play
Checkers
Chinook ended 40-year reign of human world-champion Marion Tinsley in 1994.
Chess
Deep blue defeated human world-champion Garry Kasparov in a six-game match in
1997.
GO
AlphaGo developed by Alphabet Inc. ‘s Google DeepMind, beats Ke Jie, the world
No. 1 ranked Go player at the time, in 2017.
See (e.g.) http://www.cs.ualberta.ac/~games for more information's.
23. Conclusion
Game playing can be effectively modelled as a search problem.
Game tree represent alternate computer/opponent moves.
Evaluation functions estimate the quality of a given board configuration for
the MAX player.
Minimax is a approach which choose move by assuming that opponent will
always choose the move which is best for them.
Alpha-beta is a procedure which can prune large parts of the search tree and
allow search to go deeper.
For many very well known games, computer algorithms based on heuristic
search match or out-perform the human world expert.
