scholarly journals Review of Kalah Game Research and the Proposition of a Novel Heuristic–Deterministic Algorithm Compared to Tree-Search Solutions and Human Decision-Making

Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 34
Author(s):  
Libor Pekař ◽  
Radek Matušů ◽  
Jiří Andrla ◽  
Martina Litschmannová
Keyword(s):  
Heuristic Algorithm ◽  
Deterministic Algorithm ◽  
Tree Search ◽  
Board Game ◽  
Benchmark Tests ◽  
Human Decision ◽  
Multiple Case ◽  
Human Opponent ◽  
Algorithm Verification ◽  
Human Player

The Kalah game represents the most popular version of probably the oldest board game ever—the Mancala game. From this viewpoint, the art of playing Kalah can contribute to cultural heritage. This paper primarily focuses on a review of Kalah history and on a survey of research made so far for solving and analyzing the Kalah game (and some other related Mancala games). This review concludes that even if strong in-depth tree-search solutions for some types of the game were already published, it is still reasonable to develop less time-consumptive and computationally-demanding playing algorithms and their strategies Therefore, the paper also presents an original heuristic algorithm based on particular deterministic strategies arising from the analysis of the game rules. Standard and modified mini–max tree-search algorithms are introduced as well. A simple C++ application with Qt framework is developed to perform the algorithm verification and comparative experiments. Two sets of benchmark tests are made; namely, a tournament where a mid–experienced amateur human player competes with the three algorithms is introduced first. Then, a round-robin tournament of all the algorithms is presented. It can be deduced that the proposed heuristic algorithm has comparable success to the human player and to low-depth tree-search solutions. Moreover, multiple-case experiments proved that the opening move has a decisive impact on winning or losing. Namely, if the computer plays first, the human opponent cannot beat it. Contrariwise, if it starts to play second, using the heuristic algorithm, it nearly always loses.

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

scholarly journals Association of Game Events with Facial Animations of Computer-Controlled Virtual Characters Based on Probabilistic Human Reaction Modeling

2020 ◽  
Vol 10 (16) ◽  
pp. 5636
Author(s):  
Wafaa Alsaggaf ◽  
Georgios Tsaramirsis ◽  
Norah Al-Malki ◽  
Fazal Qudus Khan ◽  
Miadah Almasry ◽  
...  
Keyword(s):  
Facial Expression ◽  
Virtual Environments ◽  
Facial Expressions ◽  
Computer Games ◽  
Virtual Agents ◽  
Board Game ◽  
Virtual Character ◽  
Virtual Characters ◽  
Computer Controlled ◽  
Human Player

Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events.

Superhuman Intelligence

2020 ◽  
pp. 203-214
Author(s):  
Chris Bleakley
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computer Program ◽  
General Purpose ◽  
Board Game ◽  
High Profile ◽  
Artificial Neural ◽  
World Champion ◽  
Human Player

Chapter 12 is the story of AlphaGo – the first computer program to defeat a top human player at the board game Go. On March 19, 2016, grandmaster Lee Sedol took on AlphaGo for a US$1 million prize in a best of five match. Experts expected that it would be easy money for Sedol. To most observers surprise, AlphaGo swept the first three games to win the match. AlphaGo was based on deep artificial neural networks (ANNs). The networks were trained with 30 million example moves followed 1.2 million games played against itself. AlphaGo was the creation of a London based company named Deep Mind Technologies. Founded in 2010 and acquired by Google 2014, DeepMind’s made a succession of high profile breakthroughs in artificial intelligence. Recently, their AlphaZero ANN displayed signs of general-purpose intelligence. It learned to play Chess, Shogi, and Go to world champion level in a few days.

scholarly journals Predicting Game Difficulty and Engagement Using AI Players

2021 ◽  
Vol 5 (CHI PLAY) ◽  
pp. 1-17
Author(s):  
Shaghayegh Roohi ◽  
Christian Guckelsberger ◽  
Asko Relas ◽  
Henri Heiskanen ◽  
Jari Takatalo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Accuracy ◽  
Selection Strategy ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Average Performance ◽  
Novel Approach ◽  
Player Modelling ◽  
Human Player

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. We have previously demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.

scholarly journals An Entropy-Guided Monte Carlo Tree Search Approach for Generating Optimal Container Loading Layouts

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 866 ◽  
Author(s):  
Richard Cant ◽  
Ayodeji Remi-Omosowon ◽  
Caroline Langensiepen ◽  
Ahmad Lotfi
Keyword(s):  
Monte Carlo ◽  
Deterministic Algorithm ◽  
Computational Time ◽  
Entropy Measure ◽  
Tree Search ◽  
Container Loading ◽  
Monte Carlo Tree Search ◽  
Spatial Entropy ◽  
Novel Approach ◽  
Search Approach

In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.

Solving Uncapacitated Facility Location Problem Using Heuristic Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 18-50 ◽  
Author(s):  
Soumen Atta ◽  
Priya Ranjan Sinha Mahapatra ◽  
Anirban Mukhopadhyay
Keyword(s):  
Facility Location ◽  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Location Problem ◽  
Heuristic Algorithms ◽  
Randomized Algorithm ◽  
Facility Location Problem ◽  
Deterministic Algorithm ◽  
Data Sets ◽  
Uncapacitated Facility Location Problem

A well-known combinatorial optimization problem, known as the uncapacitated facility location problem (UFLP) is considered in this article. A deterministic heuristic algorithm and a randomized heuristic algorithm are presented to solve UFLP. Though the proposed deterministic heuristic algorithm is very simple, it produces good solution for each instance of UFLP considered in this article. The main purpose of this article is to process all the data sets of UFLP available in the literature using a single algorithm. The proposed two algorithms are applied on these test instances of UFLP to determine their effectiveness. Here, the solution obtained from the proposed randomized algorithm is at least as good as the solution produced by the proposed deterministic algorithm. Hence, the proposed deterministic algorithm gives upper bound on the solution produced by the randomized algorithm. Although the proposed deterministic algorithm gives optimal results for most of the instances of UFLP, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this article including those for which the deterministic algorithm fails to achieve the optimal solutions.

An Autonomous Robot Playing the Board Game Checkers

1998 ◽  
Vol 35 (1) ◽  
pp. 14-28
Author(s):  
M. Zribi ◽  
E. Sung
Keyword(s):  
Training Program ◽  
Autonomous Robot ◽  
Practical Training ◽  
Board Game ◽  
Second Year ◽  
Human Player

In this paper, a project completed by a group of second year students during an in-house practical training program is described. The objective of this project is to produce a system in which a robot is capable of autonomously playing the board game, checkers, against a human player.

scholarly journals Performance of Monte Carlo Tree Search Algorithms when Playing the Game Ataxx

2018 ◽  
Author(s):  
Leonardo F. R. Ribeiro ◽  
Daniel R. Figueiredo
Keyword(s):  
Monte Carlo ◽  
Search Algorithms ◽  
Tree Search ◽  
Board Game ◽  
Promising Technique ◽  
Game Tree ◽  
State Spaces ◽  
Trade Off ◽  
Monte Carlo Tree Search ◽  
Large State

Monte Carlo Tree Search (MCTS) has recently emerged as a promising technique to play games with very large state spaces. Ataxx is a simple two-player board game with large and deep game tree. In this work, we apply different MCTS algorithms to play the game Ataxx and evaluate its performance against different adversaries (e.g., minimax2). Our analysis highlights one key aspect of MCTS, the trade-off between samples (and accuracy) and chances of winning the game which translates to a trade-off between the delay in making a move and chances of winning.

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