Turn-Based War Chess Model and Its Search Algorithm per Turn

War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games (TBS) and is studied in this paper. First, a common game model is proposed through various existing war chess types. Based on the model, we propose a theory frame involving combinational optimization on the one hand and game tree search on the other hand. We also discuss a key problem, namely, that the number of the branching factors of each turn in the game tree is huge. Then, we propose two algorithms for searching in one turn to solve the problem: (1) enumeration by order; (2) enumeration by recursion. The main difference between these two is the permutation method used: the former uses the dictionary sequence method, while the latter uses the recursive permutation method. Finally, we prove that both of these algorithms are optimal, and we analyze the difference between their efficiencies. An important factor is the total time taken for the unit to expand until it achieves its reachable position. The factor, which is the total number of expansions that each unit makes in its reachable position, is set. The conclusion proposed is in terms of this factor: Enumeration by recursion is better than enumeration by order in all situations

On the Impact of Information Technologies on Society: an Historical Perspective through the Game of Chess

The game of chess as always been viewed as an iconic representation of intellectual prowess. Since the very beginning of computer science, the challenge of being able to program a computer capable of playing chess and beating humans has been alive and used both as a mark to measure hardware/software progresses and as an ongoing programming challenge leading to numerous discoveries. In the early days of computer science it was a topic for specialists. But as computers were democratized, and the strength of chess engines began to increase, chess players started to appropriate to themselves these new tools. We show how these interactions between the world of chess and information technologies have been herald of broader social impacts of information technologies. The game of chess, and more broadly the world of chess (chess players, literature, computer softwares and websites dedicated to chess, etc.), turns out to be a surprisingly and particularly sharp indicator of the changes induced in our everyday life by the information technologies. Moreover, in the same way that chess is a modelization of war that captures the raw features of strategic thinking, chess world can be seen as small society making the study of the information technologies impact easier to analyze and to grasp

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Application of the Monte-Carlo Tree Search to Multi-Action Turn-Based Games with Hidden Information

Traditional search algorithms struggle when applied to complex multi-action turn-based games. The introduction of hidden information further increases domain complexity. The Monte-Carlo Tree Search (MCTS) algorithm has previously been applied to multi-action turn-based games, but not multi-action turn-based games with hidden information. This thesis compares several Monte Carlo Tree Search (MCTS) extensions (Determinized/Perfect Information Monte Carlo, Multi-Observer Information Set MCTS, and Belief State MCTS) in TUBSTAP, an open-source multi-action turn-based game, modified to include hidden information via fog-of-war

Online evolution for multi-action adversarial games

We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems

Fifth Aeon – A.I Competition and Balancer

Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that do not upset the balance of an existing collectible card game. The submissions to the A.I competition include one that plays substantially better than the existing Fifth Aeon A.I with a higher winrate across multiple game formats. The balancer system also demonstrates an ability to automatically balance several types of cards against a wide variety of parameters. These results help pave the way to cheaper CCG development with more compelling A.I opponents

Quantum Limits, Computational Complexity and Philosophy – A Review: Shamaila Shafiq

Quantum computing physics uses quantum qubits (or bits), for computer’s memory or processor. They can perform certain calculations much faster than a normal computer. The quantum computers have some limitations due to which the problems belonging to NP- Complete are not solved efficiently. This paper covers effective quantum algorithm for solving NP-Complete problems through some features of complexity theory, that we can simplify some of the philosophical interest problems

Tribes: A New Turn-Based Strategy Game for AI Research

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength