Computational Intelligence In Games Research Articles

Метод оптимизации организационной структуры централизованных многоагентных систем в автоматическом режиме

The organizational structure of a multi-agent system (MAS) is a set of roles and relationships of agents, com-ponents that control their behavior, as well as rules governing the interaction of system elements. The effectiveness of the MAS largely depends on the characteristics of the organizational structure used. Existing solutions in this area have a significant disadvantage, namely low adaptability to changes in environmental parameters or adjustment of the conditions of the task, which consists in the need to restart the procedure for synthesizing the organizational structure of the MAS. The problem of automatic optimization of the organizational structure of centralized MAS in the conditions of changed environmental parameters or the task is of particular relevance for centralized MAS with a strict hierarchical structure, whose agents can be divided into two classes – agents-managers, agents-subordinates. The subject of the study is the methods of synthesis of the organizational structure of the MAS. The aim of the work is to develop a method for optimizing the organizational structure of centralized MAS in automatic mode. To achieve this goal, the concepts of primary and secondary agent resources were introduced, based on a biogeographic algorithm, a method was developed that regulates the movement of subordinate agents between groups depending on the degree of attractiveness of the agent manager (depends on the sum of estimates of the effectiveness of subordinate agents managed by him, as well as on the distance from this subordinate agent). The developed method can find its practical application in the implementation of the following tasks: inspection or patrolling (protection) of infrastructure facilities by mobile robots, the implementation of artificial intelligence in computer games.

AI Application in Computer Games

It is well-established that artificial intelligence has already been used in nearly all kinds of computer games. The purpose of most games is to entertain (though there exists some educational games which aim to teach), and AI takes the role to improve the player’s experience. First, current use of artificial intelligence in computer games is introduced, then, some AI applications which aim to promote both entertainment and interactive environment is mentioned. Then, the future of artificial intelligence to find the possibility to create a kind of AI which can not only interact with players but also improve as number and experience of players grow. Lastly, the paper will analyse the future use of AI in different fields of computer games including, but not quite, the feedback of game bugs and solutions of players’ problems.

Grid-Based City Generation for Replayability: Procedural Content Generation and Artificial Intelligence in Computer Games

Grid-Based City Generation for Replayability: Procedural Content Generation and Artificial Intelligence in Computer Games

Decision-Making Decisions: Artificial Intelligence In Computer Games

Decision-Making Decisions: Artificial Intelligence In Computer Games

Artificial Intelligence in Computer Games

В данной статье представлена разработка и исследование модели формализации процесса принятия решений в компьютерной игре с использованием методов искусственного интеллекта. Игровой искусственный интеллект реализуется с помощью достаточно ограниченного набора средств: искусственных нейронных сетей, эволюционных алгоритмов, специализированных архитектур построения систем искусственного интеллекта. Но так же нейронные сети имеют ряд недостатков. Пожалуй, самым существенным из них является сложность и большое время обучения сети. Ставится проблема разработки новых математических алгоритмов, имеющих вероятность принятия правильного решения, сравнимую с нейронными сетями, но обладающих меньшим временем обучения В данный момент отсутствует единая модель искусственной иммунной системы для разработки искусственного интеллекта в компьютерных и мобильных играх. Таким образом, ставится проблема разработки новых алгоритмов, позволяющих реализовать взаимодействие с игроком в компьютерной игре, имеющих скорость и реалистичность, сравнимую с искусственными нейронными сетями и экспертными системами и при этом обладающих меньшим временем обучения. Одним из путей решения этой задачи является разработка модели игрового искусственного интеллекта на основе искусственной иммунной системы. Игра в точки является одной из модификаций японской игры go. В данной игре позиционное стратегическое планирование сочетается с тактическим перебором вариантов. Реализация искусственного интеллекта в данной игре в виде дерева решений не является для решения данной задачи рациональным. Для упрощения поиска оптимального решения использовались эвристические правила. Для сокращения времени поиска решения был создан игровой искусственный интеллект с применением методов, основанных на принципах иммунной системы. Искусственная иммунная система представляет идеализированный вариант естественного аналога и воспроизводит ключевые составляющие природного процесса: отбор лучших антител популяции в зависимости от степени их аффинитета (близости) к антигену, клонирование антител, мутация антител. This article presents the development and study of a model for formalizing the decision-making process in a computer game using artificial intelligence methods. Game artificial intelligence is implemented using a rather limited set of tools: artificial neural networks, evolutionary algorithms, specialized architectures for building artificial intelligence systems. But neural networks also have a number of disadvantages. Perhaps the most significant of them is the complexity and long network training time. The problem is raised of developing new mathematical algorithms that have a probability of making the right decision, comparable to neural networks, but with less training time. At the moment, there is no single model of the artificial immune system for the development of artificial intelligence in computer and mobile games. Thus, the problem is raised of developing new algorithms that allow realizing interaction with the player in a computer game, having speed and realism comparable to artificial neural networks and expert systems, and at the same time having less training time. One of the ways to solve this problem is to develop a game artificial intelligence model based on an artificial immune system. The dot game is a modification of the Japanese game go. In this game, positional strategic planning is combined with tactical enumeration of options. The implementation of artificial intelligence in this game in the form of a decision tree is not rational for solving this problem. To simplify the search for the optimal solution, heuristic rules were used. To reduce the solution search time, game artificial intelligence was created using methods based on the principles of the immune system. The artificial immune system is an idealized version of the natural analogue and reproduces the key components of the natural process: selection of the best antibodies in the population depending on the degree of their affinity (proximity) to the antigen, antibody cloning, antibody mutation.

The Current State of StarCraft AI Competitions and Bots

Real-Time Strategy (RTS) games have become an increasingly popular test-bed for modern artificial intelligence techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament (SSCAIT), the Computational Intelligence in Games (CIG) competition, and the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) competition. In this paper we will give an overview of the current state of these competitions, and the bots that compete in them.

The Text-Based Adventure AI Competition

In 2016–2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games. This competition fills a gap in existing game artificial intelligence (AI) competitions that have typically focused on traditional card/board games or modern video games with graphical interfaces. By providing a platform for evaluating agents in text-based adventures, the competition provides a novel benchmark for game AI with unique challenges for natural language understanding and generation. This paper summarizes the three competitions ran in 2016–2018 (including details of open-source implementations of both the competition framework and our competitors) and presents the results of an improved evaluation of these competitors across 20 games.

Player profiling and quality assessment of dynamic car racing tracks using entertainment quantifier technique

AbstractInteractive games have been an interesting area of research and have many challenges. With the advancement in technology, games have been revolutionizing at each step per the emerging and variant interests of players. Recently, machine learning techniques are used for the generation of game content based on player's experience. The dynamic content generation in computer games based on player's experience and feedback is still a challenging task. This requires measurement of entertainment factor achieved by a player during a game. In order to measure entertainment factor, we need to incorporate human‐computer interaction by evolution of game content with respect to player's response. Optimization techniques can be used for the measurement of entertainment factor and for the generation of dynamic game content. The use of computational intelligence techniques in game development can lead to a new domain called “computational intelligence in games.” This research is focused on car racing game genre, and the paradigm selected for dynamicity is track generation of car racing game. It requires player profiling and classification of players. The optimization of track generation has been performed by using single and multiobjective genetic algorithm and particle swarm optimization. Initially, classification of player's rank based on data and theory‐driven approaches has been performed. Moreover, 3 different techniques of defining ranges or boundaries of race parameters for player's rank classification are studied. The techniques are based on crisp values, neural network, and fuzzy inference process. Then, an entertainment quantifier technique is proposed for a player after playing a certain number of games based on dynamic content generation using multiobjective genetic algorithm using standard Pareto optimal front and an epsilon (ϵ) front. In conclusion, the method proposed for quantifying entertainment can be used to analyze and classify the trend in interests of a player according to which the game itself can dynamically generate. This will keep the interest of player intact and provides maximum entertainment experience per the interest of an individual. The proposed solution can easily be used in generation of any game content and can effectively be used in accurate measurement of entertaining factor of any game.

The First MicroRTS Artificial Intelligence Competition

This article presents the results of the first edition of the microRTS (μRTS) AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, focusing on address- ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.

A Panorama of Artificial and Computational Intelligence in Games

This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: 1) the dominant AI method(s) used under each area; 2) the relation of each area with respect to the end (human) user; and 3) the placement of each area within a human–computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.

일반 비디오 게임 플레이 인공지능을 위한 GreedyUCB1기반 몬테카를로 트리 탐색

Generally, the existing Artificial Intelligence (AI) systems were designed for specific purposes and their capabilities handle only specific problems. Alternatively, Artificial General Intelligence can solve new problems as well as those that are already known. Recently, General Video Game Playing the game AI version of General Artificial Intelligence, has garnered a large amount of interest among Game Artificial Intelligence communities. Although video games are the sole concern, the design of a single AI that is capable of playing various video games is not an easy process. In this paper, we propose a GreedyUCB1 algorithm and rollout method that were formulated using the knowledge from a game analysis for the Monte-Carlo Tree Search game AI. An AI that used our method was ranked fourth at the GVG-AI (General Video Game-Artificial Intelligence) competition of the IEEE international conference of CIG (Computational Intelligence in Games) 2014.

IEEE Conference on Computational Intelligence in Games 2014

IEEE Conference on Computational Intelligence in Games 2014

IEEE Conference on Computational Intelligence in Games 2014

IEEE Conference on Computational Intelligence in Games 2014

An analysis of the structure and evolution of the scientific collaboration network of computer intelligence in games

Games constitute a research domain that is attracting the interest of scientists from numerous disciplines. This is particularly true from the perspective of computational intelligence. In order to examine the growing importance of this area in the gaming domain, we present an analysis of the scientific collaboration network of researchers working on computational intelligence in games (CIG). This network has been constructed from bibliographical data obtained from the Digital Bibliography & Library Project (DBLP). We have analyzed from a temporal perspective several properties of the CIG network at the macroscopic, mesoscopic and microscopic levels, studying the large-scale structure, the growth mechanics, and collaboration patterns among other features. Overall, computational intelligence in games exhibits similarities with other collaboration networks such as for example a log-normal degree distribution and sub-linear preferential attachment for new authors. It also has distinctive features, e.g. the number of papers co-authored is exponentially distributed, the internal preferential attachment (new collaborations among existing authors) is linear, and fidelity rates (measured as the relative preference for publishing with previous collaborators) grow super-linearly. The macroscopic and mesoscopic evolution of the network indicates the field is very active and vibrant, but it is still at an early developmental stage. We have also analyzed communities and central nodes and how these are reflected in research topics, thus identifying active research subareas.

IEEE 2013 Conference on Computational Intelligence in Games

IEEE 2013 Conference on Computational Intelligence in Games

Computational Intelligence and AI in Games: A New IEEE Transactions

The author first provides an overview of computational intelligence and AI in games. Then he describes the new IEEE Transactions, which will publish archival quality original papers in all aspects of computational intelligence and AI related to all types of games. To name some examples, these include computer and video games, board games, card games, mathematical games, games that model economies or societies, serious games with educational and training applications, and games involving physical objects such as robot football and robotic car racing. Emphasis will also be placed on the use of these methods to improve performance in, and understanding of, the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It will also include using games as a platform for building intelligent embedded agents for real-world applications. The journal builds on a scientific community that has already been active in recent years with the development of new conference series such as the IEEE Symposium on Computational Intelligence in Games (CIG) and Artificial Intelligence and Interactive Digital Entertainment (AIIDE), as well as special issues on games in journals such as the IEEE Transactions on Evolutionary Computation. When setting up the journal, a decision was made to include both artificial intelligence (AI) and computational intelligence (CI) in the title. AI seeks to simulate intelligent behavior in any way that can be programmed effectively. Some see the field of AI as being all-inclusive, while others argue that there is nothing artificial about real intelligence as exhibited by higher mammals.

Artificial Intelligence for Computer Games

Artificial intelligence (AI) in computer games covers the behaviour and decision-making process of game-playing opponents (also known as nonplayer character or NPC). Current generations of computer and video games offer an amazingly interesting testbed for AI research and new ideas. Such games combine rich and complex environments with expertly developed, stable, physics-based simulation. They are real-time and very dynamic, encouraging fast and intelligent decisions. Computer games are also often multiagents, making teamwork, competition, and NPC modelling key elements to success. In commercial games, such as action games, role-playing games, and strategy games, the behaviour of the NPC is usually implemented as a variation of simple rule-based systems. With a few exceptions, machine-learning techniques are hardly ever applied to state-of-the-art computer games. Machine-learning techniques may enable the NPCs with the capability to improve their performance by learning from mistakes and successes, to automatically adapt to the strengths and weaknesses of a player, or to learn from their opponents by imitating their tactics.

Students teaching students

This paper presents the structure of a new elective course at the University of Florida, Artificial Intelligence in Computer Games, which addresses not only the technical aspects of creating realism within computer games, but also the need for more experiences in oral communication. Key elements of this course include the students conducting a majority of the teaching and actively participating in evaluating each other.

Computational Intelligence in Games

Computational Intelligence in Games