Modelling and implementation of 9tka game with MaxN algorithm

9tka is a board game created by Adam Kaluza. The game can be played with 2 up to 4 players, with the goal of conquering as many areas in the board as possible. The aim of this research is to implement the 9tka game into a digital game that can be played on a personal computer. The implementation will include the feature to play against computer players. The rules and game’s play of 9tka is modelled, and then implemented using Java. The Artificial Intelligence (AI) of the computer player is implemented using the MaxN algorithm, which is an extension of the minimax algorithm. Several tests were done to gauge the robustness of the implemented AI. The experiments show that the AI is capable to make a move in time less than 541 milliseconds on average, across all types of players. Moreover, from eight respondents, the average amount of human wins is 2.25 out of 5 matches, across all types of players. This shows that the implemented AI on computer player has a better chance to defeat human opponents

PERFECT RECALL AND PRUNING IN GAMES WITH IMPERFECT INFORMATION 1

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72986/1/j.1467-8640.1996.tb00256.x.pd

Opponent-Pruning Paranoid Search

This paper proposes a new search algorithm for fully observable, deterministic multiplayer games: Opponent-Pruning Paranoid Search (OPPS). OPPS is a generalization of a state-of-the-art technique for this class of games, Best-Reply Search (BRS+). Just like BRS+, it allows for Alpha-Beta style pruning through the paranoid assumption, and both deepens the tree and reduces the pessimism of the paranoid assumption through pruning of opponent moves. However, it introduces

Alternância entre competiçao e colaboraçao para promover o aprendizado por meio de heurísticas de jogos

Orientador: Alexandre Ibrahim DireneInclui apendiceDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2006Inclui bibliografi

Artificial Intelligence in Pursuit-evasion Games, Specifically in the Scotland Yard Game

This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the possible options. The heuristic algorithm then uses a process of elimination to detemine the best possible detective moves by running an appropriately constructed minimum cost flow maximum flow instance. The heuristic algorithm was tested in a series of experiments, in which the algorithm achieved a 57 win rate. This win rate was achieved using a random starting position for each of the pursuer detectives as well as for the evader, Mr. X. When Mr. X started at an easily accessible location, namely position 146, the pursuing detectives were able to capture him 62% of the time. These results show promise for this heuristic in pursuer-evader games like Scotland Yard

Γενικοί Παίκτες: Αλγόριθμοι και Τεχνικές

Η παρούσα διπλωματική εργασία έχει σαν αντικείμενο τη μελέτη γενικών παικτών (General Game Players). Οι γενικοί παίκτες είναι υπεύθυνοι για το παίξιμο οποιουδήποτε παιχνιδιού, αρκεί οι κανόνες του να περιγράφονται σε μια συγκεκριμένη μορφή – γλώσσα. Η αρχή για την υλοποίηση γενικών παικτών έγινε τη δεκαετία του 2000, όμως η ιδέα γεννήθηκε πολλά χρόνια πριν, σε πολύ πρώιμη μορφή. Στην εργασία αυτή γίνεται μια καταγραφή της βασικής αρχιτεκτονικής ενός γενικού παίκτη, των εξελίξεων που υπάρχουν στον τομέα τα τελευταία χρόνια, καθώς και των δυσκολιών που υπάρχουν ακόμα. Προκειμένου να γίνει πιο αποτελεσματική η μελέτη, στα πλαίσια της διπλωματικής εργασίας, αναπτύχθηκε ο γενικός παίκτης SweetGGP, ώστε να εφαρμοστούν στην πράξη, όσο περισσότερο γίνεται, οι γνώσεις που αποκτήθηκαν, και να εξαχθούν συμπεράσματα. Τέλος, αναπτύχθηκαν επεκτάσεις στη γλώσσα περιγραφής παιχνιδιών, προκειμένου να γίνει ευκολότερη η περιγραφή ορισμένων ομάδων παιχνιδιών.The object of this master thesis is the study of general game players. General game players are able to play any game, if the game’s rules are represented in a certain format – language. The development of the first general game players began on 2000s, but the idea was born many years earlier, in an early version. In this thesis, the basic architecture of a general game player, the developments in recent years, and the difficulties found are presented. In order for the study to be more accurate, the SweetGGP general game player was developed, in order to use the knowledge gained, as much as possible, and come to conclusions. Finally, extensions of the game description language were developed, so that the description of some groups of games becomes easier

Competitive multi-agent search

textWhile evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this dissertation formalizes human creative problem solving as competitive multi-agent search. It differs from existing single-agent and team-search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape caused by these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for competitive multi-agent search. This hypothesis is verified in experiments using an abstract domain based on the NK model, i.e. partially correlated and tunably rugged fitness landscapes, and a concrete domain in the form of a social innovation game. In both domains, different specialized strategies are evolved for each different competitive environment, and also strategies that generalize across environments. Strategies evolved in the abstract domain are more effective and more complex than hand-designed strategies and one based on traditional tree search. Using a novel spherical visualization of the fitness landscapes of the abstract domain, insight is gained about how successful strategies work, e.g. by tracking positive changes in the landscape. In the concrete game domain, human players were modeled using backpropagation, and used as opponents to create environments for evolution. Evolved strategies scored significantly higher than the human models by using a different proportion of actions, providing insights into how performance could be improved in social innovation domains. The work thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future.Computer Science

4人でプレイするBlokus のAI プレイヤの強化学習

　4人でプレイするBlokusは4人非ゼロ和完全情報ゲームであり、行動集合の大きさは1,000程度に達する。様々な2人ゲームでは、強化学習と深層学習を用いたAlphaZeroなどプロプレイヤに勝つほど強力な人工知能 (AI)プレイヤが既に開発されている。しかし、多人数完全情報ゲームでは深層学習を用いたAIプレイヤの開発事例はあまり報告されていなく、特にBlokusではそのような例はまだ報告はされていない。　多人数完全情報ゲームの木探索アルゴリズムは既に複数考案されており、Blokusでの適用例が報告されているものはMAXN探索、Paranoid探索、Best-Reply探索がある。さらに、それらにモンテカルロ法を組合せる手法も存在する。　本研究の目的は、4人でプレイするBlokusの強いAIプレイヤを開発することである。BlokusのAIプレイヤの開発の手法として、上級者の棋譜を用いた教師あり学習は困難である。そこで、2人ゼロ和完全情報ゲームのAIプレイヤの学習アルゴリズムであるAlphaZeroを修正して、モンテカルロ木探索 (MCTS) と強化学習、深層学習を組合せた手法によって、ランダムな着手から改善していくBlokusZeroを提案する。主な修正点はニューラルネットワーク (NN)の構成と、MCTSの探索木である。　BlokusZeroの性能評価を行うために先行研究のBlokusAIであるMCTS-MAXNも実装し、学習したBlokusZeroプレイヤ1人とMCTS-MAXNプレイヤ3人で対戦実験を行って勝率を計測した。その結果、ゲーム開始から4手は一様ランダム、各プレイヤのMCTSのシミュレーション回数が同じという条件の下で、BlokusZeroの勝率はMCTS-MAXNの勝率を上回った。電気通信大学201