Real-time Strategy Games Research Articles

Challenges and Opportunities in Game Artificial Intelligence Education Using Angry Birds

Games have been an important tool for motivating undergraduate students majoring in computer science and engineering. However, it is difficult to build an entire game for education from scratch, because the task requires high-level programming skills and expertise to understand the graphics and physics. Recently, there have been many different game artificial intelligence (AI) competitions, ranging from board games to the state-of-the-art video games (car racing, mobile games, first-person shooting games, real-time strategy games, and so on). The competitions have been designed such that participants develop their own AI module on top of public/commercial games. Because the materials are open to the public, it is quite useful to adopt them for an undergraduate course project. In this paper, we report our experiences using the Angry Birds AI Competition for such a project-based course. In the course, teams of students consider computer vision, strategic decision-making, resource management, and bug-free coding for their outcome. To promote understanding of game contents generation and extensive testing on the generalization abilities of the student’s AI program, we developed software to help them create user-created levels. Students actively participated in the project and the final outcome was comparable with that of successful entries in the 2013 International Angry Birds AI Competition. Furthermore, it leads to the development of a new parallelized Angry Birds AI Competition platform with undergraduate students aiming to use advanced optimization algorithms for their controllers.

Real Time Strategy Games: A Reinforcement Learning Approach

Abstract In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning1 and SARSA1 algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on Real Time Strategy (RTS) game called BattleCity. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works.

Attractiveness of real time strategy games

Game refinement idea is a unique theory that has been proposed based on the uncertainty of game outcome. A game refinement measure was derived from the game information progress model and has been applied in the domains such as board games and sports games. The present challenge is to apply the game refinement theory in the domain of RTS games. To do so, we use StarCraft II as a testbed and introduce a concept of strategy tree in order to construct a game tree of a RTS game. Then, game refinement values are calculated and compared with other type of games. It is found that StarCraft II has a zone value of game refinement.

Geometrical vs topological measures for the evolution of aesthetic maps in a RTS game

This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.

Playing a puzzle video game with changing requirements improves executive functions

Recent research suggests a causal link between action video game playing and enhanced attention and visual-perceptual skills. In contrast, evidence linking action video games and enhanced executive function is equivocal. We investigated whether action and non-action video games enhance executive function. Fifty-five inexperienced video game players played one of four different games: an action video game (Modern Combat), a physics-based puzzle game (Cut the Rope), a real-time strategy game (Starfront Collision), and a fast paced arcade game (Fruit Ninja) for 20h. Three pre and post training tests of executive function were administered: a random task switching, a flanker, and a response inhibition task (Go/No-go). Only the group that trained on the physics-based puzzle game significantly improved in all three tasks relative to the pre-test. No training-related improvements were seen in other groups. These results suggest that playing a complex puzzle game that demands strategizing, reframing, and planning improves several aspects of executive function.

Neuroevolution Based Multi-Agent System with Ontology Based Template Creation for Micromanagement in Real-Time Strategy Games

This paper presents a multi-agent system that handles unit micromanagement using online machine learning in real time strategy games. We used rtNEAT algorithm in order to obtain customized neural network topologies, thus avoiding to complex network architecture. We use an ontology based template to create suitable input and outputs for unit agents enabling them to cooperate and form teams for their mutual benefit and eliminating communication overhead. The AI system was implemented using the JADE framework and the BWAPI handled communication between our system and the game. We have chosen Starcraft as a testbed. As a baseline we compared the in game AI as well as several other AI solutions that use adaptive mechanisms. DOI: http://dx.doi.org/10.5755/j01.itc.43.1.4600

Special issue on real-time strategy games

Special issue on real-time strategy games

A Micromanagement Task Allocation System for Real-Time Strategy Games

Real-time strategy (RTS) game play is a combination of strategy and micromanagement. While strategy is clearly important, the success of a strategy can depend greatly on effective micromanagement. Recent years have seen an increase in work focusing on micromanagement in RTS AI, but the great majority of these works have focused on policies for individual units or very specific situations, while very little work has aimed to address the need for a broadly applicable structure for unit group coordination. This paper conceptualizes RTS group level micromanagement as a multiagent task allocation (TA) problem, and proposes the micromanagement task allocation system (MTAS) as a framework to bridge the gap between strategy and unit level micromanagement. MTAS fits into the common layered RTS AI structure, and can thus, in principle, be integrated into any existing system based on this layered structure. We describe the integration of MTAS into E323AI (an existing AI player for the spring RTS engine), and provide empirical results demonstrating that MTAS leads to statistically significant improvement in performance on the popular RTS game Balanced Annihilation.

RTSMate

Real Time Strategy (RTS) games can be very challenging, especially to novice users, who are normally overwhelmed by the dynamic, distributed, and multi-objective structure of these games. In this paper we present RTSMate, an advice system designed to help the player of an RTS game. Using inference mechanisms to reason about the game state and a decision tree to encode its knowledge, RTSMate helps the player by giving him/her tactical and strategical tips about the best actions to be taken according to the current game state, aiming at improving player's performance. This paper describes the main ideas behind the system, its implementation, and the experiments performed using the system in a real game environment. Results show that RTSMate fulfills its objective: most players considered the system useful and were able to improve their performance by using it.

Special issue on real-time strategy games

Special issue on real-time strategy games

Virtual player design using self-learning via competitive coevolutionary algorithms

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.

Efficient processing of optimal meeting point queries in Euclidean space and road networks

Finding an optimal meeting point (OMP) for a group of people (or a set of objects) at different locations is an important problem in spatial query processing. There are many real-life applications related to this problem, such as determining the location of a conference venue, deciding the pick-up location of a tourist bus, and planing tactics of artificial intelligence in real-time strategy games. Formally, given a set $$Q$$ Q of query points in a spatial setting $$P$$ P , an OMP query fetches the point $$o\in P$$ o ? P that minimizes a cost function defined over the distances from $$o$$ o to all points in $$Q$$ Q . Since there are infinitely many locations in a given space setting, it is infeasible to examine all of them to find the OMP, and thus, the problem is challenging. In this paper, we study OMP queries in the following two spatial settings which are common in real-life applications: Euclidean space and road networks. In the setting of Euclidean space, we propose a general framework for answering all OMP query variants and also identify the best algorithms for particular types of OMP queries in the literature. In the setting of road networks, we study how to access only part of the road network and examine part of the candidates. Specifically, we explore two pruning techniques, namely Euclidean distance bound and threshold algorithm, which help improve the efficiency of OMP query processing. Extensive experiments are conducted to demonstrate the efficiency of our proposed approaches on both real and synthetic datasets.

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open.

Profile, Gaming Usage and Purposes of Gaming of Internet Café Users in Manila

This descriptive-exploratory study attempted to give the readers a portrait of cyber café gamers in Manila. It determined the profile of gamers, their gaming usage, and their purposes of cyber café gaming. Descriptive statistics revealed that most of the respondents were Manila settlers, students, pursuing or had obtained college degrees, male, young, Roman Catholic, single, belonged to middle-income class, and played games in cyber cafés in the afternoon once to twice a week. One-way chi-square showed that frequency of gaming was not equally distributed in a week and gamers showed tendency to play games in a cyber in a particular time of the day. Real-time strategy games were the most frequently played games in cyber cafés. To recreate, to relieve boredom, and to have fun were the top three reasons in playing games in cyber cafés. Conclusions and directions for future research were also presented.

Seeing Change in Time: Video Games to Teach about Temporal Change in Scientific Phenomena

This article explores how learning biological concepts can be facilitated by playing a video game that depicts interactions and processes at the subcellular level. Particularly, this article reviews the effects of a real-time strategy game that requires players to control the behavior of a virus and interact with cell structures in a way that resembles the actual behavior of biological agents. The evaluation of the video game presented here aims at showing that video games have representational advantages that facilitate the construction of dynamic mental models. Ultimately, the article shows that when video game’s characteristics come in contact with expert knowledge during game design, the game becomes an excellent medium for supporting the learning of disciplinary content related to dynamic processes. In particular, results show that students who participated in a game-based intervention aimed at teaching biology described a higher number of temporal-dependent interactions as measured by the coding of verbal protocols and drawings than students who used texts and diagrams to learn the same topic.

Real-Time Strategy Game Training: Emergence of a Cognitive Flexibility Trait

Training in action video games can increase the speed of perceptual processing. However, it is unknown whether video-game training can lead to broad-based changes in higher-level competencies such as cognitive flexibility, a core and neurally distributed component of cognition. To determine whether video gaming can enhance cognitive flexibility and, if so, why these changes occur, the current study compares two versions of a real-time strategy (RTS) game. Using a meta-analytic Bayes factor approach, we found that the gaming condition that emphasized maintenance and rapid switching between multiple information and action sources led to a large increase in cognitive flexibility as measured by a wide array of non-video gaming tasks. Theoretically, the results suggest that the distributed brain networks supporting cognitive flexibility can be tuned by engrossing video game experience that stresses maintenance and rapid manipulation of multiple information sources. Practically, these results suggest avenues for increasing cognitive function.

SEPIA: A Scalable Game Environment for Artificial Intelligence Teaching and Research

We describe a game environment we have developed that we call the Strategy Engine for Programming Intelligent Agents (SEPIA). SEPIA is based on real-time strategy games, but modified extensively to preferentially support the development of artificial agents rather than human play. Through flexible configuration options, SEPIA is designed to be pedagogically scalable: suitable for use at the undergraduate and graduate levels, and also as a research testbed. We also describe assignments and our experiences with this environment in undergraduate and graduate classes.

Evolving Controllers for Simulated Car Racing Using Differential Evolution

This paper presents an initial approach for creating autonomous controllers for the car racing game using a hybrid technique. The Differential Evolution (DE) algorithm is combined with Feed-forward Artificial Neural Networks (FFANNs) to generate the required intelligent controllers in a well-known car racing game, namely The Open Racing Car Simulator (TORCS). TORCS is used as a platform in most of the IEEE conference competitions. The main objective of this research is to test the feasibility of the DE implementation in TORCS platform. The literature showed that the application of DE in Real Time Strategy game returned promising results in evolving the required strategy gaming controllers. Interestingly, there is still no study thus far that has been conducted in applying DE into TORCS game platform. This research result shows that DE performed well in TORCS even though a very simple fitness function was used. This indicates that DE has well-tuned the neural network weights to generate optimal and sub-optimal controllers in TORCS.

A Multi-objective Genetic Algorithm for Build Order Optimization in StarCraft II

This article presents a modified version of the multi-objective genetic algorithm NSGA II in order to find optimal opening strategies in the real-time strategy game StarCraft II. Based on an event-driven simulator capable of performing an accurate estimate of in-game build-times the quality of different build lists can be judged. These build lists are used as chromosomes within the genetic algorithm. Procedural constraints e.g. given by the Tech-Tree or other game mechanisms, are implicitly encoded into them. Typical goals are to find the build list producing most units of one or more certain types up to a certain time (Rush) or to produce one unit as early as possible (Tech-Push). Here, the number of entries in a build list varies and the objective values have in contrast to the search space a very small diversity. We introduce our game simulator including its graphical user interface, the modifications necessary to fit the genetic algorithm to our problem, test our algorithm on different Tech-Pushes and Rushes for all three races, and validate it with empirical data of expert StarCraft II players.

On Adversarial Policy Switching with Experiments in Real-Time Strategy Games

Given a Markov game, it is often possible to hand-code or learn a set of policies that capture a diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state. We consider how to use such policy sets and simulators to make decisions in large Markov games such as real-time strategy (RTS) games. Prior work has considered the problem using an approach we call minimax policy switching. At each decision epoch, all policy pairs are simulated against each other from the current state, and the minimax policy is chosen and used to select actions until the next decision epoch. While intuitively appealing, our first contribution is to show that this switching policy can have arbitrarily poor worst case performance. Our second contribution is to describe a simple modification, whose worst case performance is provably no worse than the minimax fixed policy in the set. Our final contribution is to conduct experiments with these algorithms in the domain of RTS games using both an abstract game engine that we can exactly simulate and a real game engine that we can only approximately simulate. The results show the effectiveness of policy switching when the simulator is accurate, and highlight challenges in the face of inaccurate simulations.