scholarly journals Design of Checkers Game Using Alpha-Beta Pruning Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 279-295
Author(s):  
Achmad Naufal Wijaya Jofanda ◽  
Mohamad Yasin
Keyword(s):  
Artificial Intelligence ◽  
Optimization Technique ◽  
Modern Time ◽  
Board Game ◽  
Pruning Algorithm ◽  
Search Results ◽  
Pruning Method ◽  
Medium Level ◽  
Alpha Beta

Checkers is a board game that is played by two people which has a purpose to defeat the opponent by eating all the opponent's pieces or making the opponent unable to make a move. The sophistication of technology at this modern time makes the checkers game can be used on a computer even with a smartphone. The application of artificial intelligence in checkers games makes the game playable anywhere and anytime. Alpha Beta Pruning is an optimization technique from the Minimax Algorithm that can reduce the number of branch/node extensions to get better and faster step search results. In this study, a checkers game based on artificial intelligence will be developed using the alpha-beta pruning method. This research is expected to explain in detail how artificial intelligence works in a game. Alpha-beta pruning was chosen because it can search for the best steps quickly and precisely. This study tested 10 respondents to play this game. The results show that the player's win rate was 60% at the easy level, 40% at the medium level, and 20% at the hard level. Besides that, the level of interest in this game was 80% being entertained and 20% feeling ordinary.

scholarly journals A Synthetic Player for Ayὸ Board Game Using Alpha-Beta Search and Learning Vector Quantization

2016 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Oluwatobi, A. Ayilara ◽  
Anuoluwapo, O. Ajayi ◽  
Kudirat, O. Jimoh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Vector Quantization ◽  
Search Algorithm ◽  
Learning Vector Quantization ◽  
Game Playing ◽  
Learning Approaches ◽  
Board Game ◽  
Significant Interest ◽  
Alpha Beta

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm.

scholarly journals IMPLEMENTASI ALGORITMA NEGASCOUT PADA PERMAINAN NINE MEN’S MORRIS

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Dany Kurniawan ◽  
Rosa Delima ◽  
Nugroho Agus Haryono
Keyword(s):  
Artificial Intelligence ◽  
Application Development ◽  
Pruning Algorithm ◽  
Technology Knowledge ◽  
Alpha Beta

Application development nowadays many are using technology/knowledge of Artificial Intelligence study. This research tries to make an intelligence agent that can play Nine Men’s Morris game using Negascout algorithm. The purpose of this research are to compare the effectiveness of Negascout algorithm in exploring the best move along with time that required, compared with Alpha-Beta Pruning algorithm.  The result from this research are Negascout algorithm explores fewer nodes and require less time in the search of the best move compared with Alpha-Beta Pruning algorithm.

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

scholarly journals Application of Combinatorial Techniques to the Ghanaian Board Game Zaminamina Draft

2019 ◽  
Vol 12 (1) ◽  
pp. 159-175
Author(s):  
Elvis Kobina Donkoh ◽  
Rebecca Davis ◽  
Emmanuel D.J Owusu-Ansah ◽  
Emmanuel A. Antwi ◽  
Michael Mensah
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Models ◽  
Decision Makers ◽  
Board Game ◽  
Rational Decision ◽  
Way Of Life ◽  
Mathematical Technique ◽  
Contemporary Culture ◽  
Conflict And Cooperation ◽  
The Matrix

Games happen to be a part of our contemporary culture and way of life. Often mathematical models of conflict and cooperation between intelligent rational decision-makers are studied in these games. Example is the African board game ’Zaminamina draft’ which is often guided by combinatorial strategies and techniques for winning. In this paper we deduce an intelligent mathematical technique for playing a winning game. Two different starting strategies were formulated; center starting and edge or vertex starting. The results were distorted into a 3x3 matrix and elementary row operations were performed to establish all possible wins. MatLab was used to distort the matrix to determine the diagonal wins. A program was written using python in artificial intelligence (AI) to help in playing optimally

Information Technology in the Search for Employment

2021 ◽  
pp. 252-267
Author(s):  
Pablo Chamoso ◽  
Alfonso González-Briones ◽  
Fancisco José García-Peñalvo
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Information Processing ◽  
State Of The Art ◽  
Developed Countries ◽  
Search Results ◽  
Technological Advances ◽  
Career Opportunities ◽  
Job Opportunities

Employability is one of the main concerns of the citizens of developed countries. Over the last 10 years, it has become popular to use technology to find employment and better career opportunities. Currently, there are many technology-powered tools available which offer their users (candidates and companies) the possibility of finding the best job opportunities/employees. However, technology is becoming increasingly advanced and current employment-oriented websites must keep up with those standards. Thanks to the computing and information processing capabilities provided by artificial intelligence, today's websites are not mere directories of jobs and candidates; instead, they make it possible to automatically filter search results according to the characteristics of candidates and jobs. This chapter presents a review of state-of-the-art technologies aimed at improving employability and analyzes the technological advances in this sector.

Recommender Systems in Digital Libraries Using Artificial Intelligence and Machine Learning

2021 ◽  
pp. 162-178
Author(s):  
Namik Delilovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Libraries ◽  
Full Text ◽  
Text Search ◽  
Full Text Search ◽  
Artificial Intelligence Techniques ◽  
Search Results ◽  
Advanced Search ◽  
Search Field

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.

Modelling a Human-Like Bot in a First Person Shooter Game

2015 ◽  
Vol 6 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Antonio Miguel Mora ◽  
Francisco Aisa ◽  
Pablo García-Sánchez ◽  
Pedro Ángel Castillo ◽  
Juan Julián Merelo
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Agents ◽  
First Person ◽  
Human Expert ◽  
Medium Level ◽  
First Person Shooter ◽  
Human Player

Autonomous agents in videogames, usually called bots, have tried to behave as human players from their emergence more than 20 years ago. They normally try to model a part of a human expert player's knowledge with respect to the game, trying to become a competitive opponent or a good partner for other players. This paper presents a deep description of the design of a bot for playing 1 vs. 1 Death Match mode in the first person shooter Unreal Tournament™ 2004 (UT2K4). This bot uses a state-based Artificial Intelligence model which emulates a big part of the behavior/knowledge (actions and tricks) of an expert human player in this mode. This player has participated in international UT2K4 championships. The behavioral engine considers primary and secondary actions, and uses a memory approach. It is based in an auxiliary database for learning about the fighting arena, so it stores weapons and items locations once the bot has discovered them, as a human player would do. This so-called Expert Bot has yielded excellent results, beating the game default bots even in the hardest difficulty, and being a very hard opponent for medium-level human players.

scholarly journals Explainable AI and Reinforcement Learning—A Systematic Review of Current Approaches and Trends

2021 ◽  
Vol 4 ◽  
Author(s):  
Lindsay Wells ◽  
Tomasz Bednarz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Reinforcement Learning ◽  
User Studies ◽  
Human In The Loop ◽  
Search Results ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai ◽  
Immersive Visualization

Research into Explainable Artificial Intelligence (XAI) has been increasing in recent years as a response to the need for increased transparency and trust in AI. This is particularly important as AI is used in sensitive domains with societal, ethical, and safety implications. Work in XAI has primarily focused on Machine Learning (ML) for classification, decision, or action, with detailed systematic reviews already undertaken. This review looks to explore current approaches and limitations for XAI in the area of Reinforcement Learning (RL). From 520 search results, 25 studies (including 5 snowball sampled) are reviewed, highlighting visualization, query-based explanations, policy summarization, human-in-the-loop collaboration, and verification as trends in this area. Limitations in the studies are presented, particularly a lack of user studies, and the prevalence of toy-examples and difficulties providing understandable explanations. Areas for future study are identified, including immersive visualization, and symbolic representation.

scholarly journals Sistem Cerdas Permainan Papan The Battle Of Honor dengan Decision Making dan Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Arnan Dwika Diasmara ◽  
Aditya Wikan Mahastama ◽  
Antonius Rachmat Chrismanto
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Intelligent System ◽  
Third Party ◽  
Case Based Reasoning ◽  
Board Game ◽  
Rule Based ◽  
Case Based ◽  
Rule Based Systems

Abstract. Intelligent System of the Battle of Honor Board Game with Decision Making and Machine Learning. The Battle of Honor is a board game where 2 players face each other to bring down their opponent's flag. This game requires a third party to act as the referee because the players cannot see each other's pawns during the game. The solution to this is to implement Rule-Based Systems (RBS) on a system developed with Unity to support the referee's role in making decisions based on the rules of the game. Researchers also develop Artificial Intelligence (AI) as opposed to applying Case-Based reasoning (CBR). The application of CBR is supported by the nearest neighbor algorithm to find cases that have a high degree of similarity. In the basic test, the results of the CBR test were obtained with the highest formulated accuracy of the 3 examiners, namely 97.101%. In testing the AI scenario as a referee, it is analyzed through colliding pieces and gives the right decision in determining victoryKeywords: The Battle of Honor, CBR, RBS, unity, AIAbstrak. The Battle of Honor merupakan permainan papan dimana 2 pemain saling berhadapan untuk menjatuhkan bendera lawannya. Permainan ini membutuhkan pihak ketiga yang berperan sebagai wasit karena pemain yang saling berhadapan tidak dapat saling melihat bidak lawannya. Solusi dari hal tersebut yaitu mengimplementasikan Rule-Based Systems (RBS) pada sistem yang dikembangkan dengan Unity untuk mendukung peran wasit dalam memberikan keputusan berdasarkan aturan permainan. Peneliti juga mengembangkan Artificial Intelligence (AI) sebagai lawan dengan menerapkan Case-Based reasoning (CBR). Penerapan CBR didukung dengan algoritma nearest neighbour untuk mencari kasus yang memiliki tingkat kemiripan yang tinggi. Pada pengujian dasar didapatkan hasil uji CBR dengan accuracy yang dirumuskan tertinggi dari 3 penguji yaitu 97,101%. Pada pengujian skenario AI sebagai wasit dianalisis lewat bidak yang bertabrakan dan memberikan keputusan yang tepat dalam menentukan kemenangan.Kata Kunci: The Battle of Honor, CBR, RBS, unity, AI

Sign in / Sign up

Export Citation Format

Share Document