Multiplier: Real-Time Strategy Unit Balancing Tool

We have built an application that integrates a technical editor feature and a custom real-time strategy game. The end users are able to use the technical editor feature for tweaking and customizing the unit attributes and progressions in the game using simple mathematical formulas, and they can play or test their tweaked formulas within the game. Various game modes in the software, which are Single Player, Multiplayer, and Simulation, can help display to the end users the results of their tweaked formulas, or users can just have fun by playing the game. The software was evaluated to see whether the software with the editor feature enabled is more attractive and appealing to the end users than the software with the editor feature disabled. The evaluation is based on the players’ feedback on the game with or without the editor. A total of 50 testers were randomly assigned into 2 groups evenly, the Tool group and the Game group. Testers assigned to the Tool group were able to customize the game unit attributes via the editor and play, while the testers in the Game group only play the game. The results from the post-test survey show both versions of the software are highly appealing to the testers, and there is no significant difference in game appeals between the Tool version and the Game version

Predictive analysis of real-time strategy games using graph mining

Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision-making or increase the efficacy of a task. Real-Time Strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real-world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such scenarios. To do this, one way to represent this type of data in order to model relationships between entities is by using graphs. The vast amount of data has resulting in complex and large graphs that are difficult to process. Hence, researchers frequently employ parallelized or distributed processing. But first, the graph data must be partitioned and assigned to multiple processors in such a way that the workload will be balanced, and inter-processor communication will be minimized. The latter problem may be complicated by the existence of edges between vertices in a graph that have been assigned to different processors. One objective of this research is to develop an accurate predictive recommendation system for multiplayer strategic games to determine recommendations for moves that a player should, and should not, make which can provide a competitive advantage. Another objective is to determine how to partition a single undirected graph in order to optimize multiprocessor load balancing and reduce the number of edges between split subgraphs --Abstract, page iv

A Real-time Strategy Agent Framework and Strategy Classifier for Computer Generated Forces

This research effort is concerned with the advancement of computer generated forces AI for Department of Defense (DoD) military training and education. The vision of this work is agents capable of perceiving and intelligently responding to opponent strategies in real-time. Our research goal is to lay the foundations for such an agent. Six research objectives are defined: 1) Formulate a strategy definition schema effective in defining a range of RTS strategies. 2) Create eight strategy definitions via the schema. 3) Design a real-time agent framework that plays the game according to the given strategy definition. 4) Generate an RTS data set. 5) Create an accurate and fast executing strategy classifier. 6) Find the best counterstrategies for each strategy definition. The agent framework is used to play the eight strategies against each other and generate a data set of game observations. To classify the data, we first perform feature reduction using principal component analysis or linear discriminant analysis. Two classifier techniques are employed, k-means clustering with k-nearest neighbor and support vector machine. The resulting classifier is 94.1% accurate with an average classification execution speed of 7.14 us. Our research effort has successfully laid the foundations for a dynamic strategy agent

Methods of multi-agent movement control and coordination of groups of mobile units in a real-time strategy games

Tato práce nabízí metodu pro reaktivní řízení jednotek v real-time strategické (RTS) počitačové hře pomocí multi-agentních potenciálových polí. Klasická RTS hra StarCraft: Broodwar byla vybrána jako testovací platforma díky jejímu postavení na konkurenční scéně umělé inteligence (UI). Nabízená umělá inteligence ovládá své jednotky pomocí umístění různých potenciálových polí na objekty a na místa v herním světě. Snahou této práce je vylepšit předchozí metody využívajicí potenciálová pole.This thesis proposes an approach to Reactive Control in Real-Time Strategy (RTS) computer games using Multi-Agent Potential Fields. The classic RTS title StarCraft: Brooodwar has been chosen as testing platform due to its status in the competitive Artificial Intelligence (AI) scene. The proposed AI controls its units by placing different types of potential fields in objects and places around the game world. This work is an attempt to improve previous methods done with Potential Field in RTS

A Multi-Objective Approach to Tactical Maneuvering Within Real Time Strategy Games

The real time strategy (RTS) environment is a strong platform for simulating complex tactical problems. The overall research goal is to develop artificial intelligence (AI) RTS planning agents for military critical decision making education. These agents should have the ability to perform at an expert level as well as to assess a players critical decision-making ability or skill-level. The nature of the time sensitivity within the RTS environment creates very complex situations. Each situation must be analyzed and orders must be given to each tactical unit before the scenario on the battlefield changes and makes the decisions no longer relevant. This particular research effort of RTS AI development focuses on constructing a unique approach for tactical unit positioning within an RTS environment. By utilizing multiobjective evolutionary algorithms (MOEAs) for finding an \optimal positioning solution, an AI agent can quickly determine an effective unit positioning solution with a fast, rapid response. The development of such an RTS AI agent goes through three distinctive phases. The first of which is mathematically describing the problem space of the tactical positioning of units within a combat scenario. Such a definition allows for the development of a generic MOEA search algorithm that is applicable to nearly every scenario. The next major phase requires the development and integration of this algorithm into the Air Force Institute of Technology RTS AI agent. Finally, the last phase involves experimenting with the positioning agent in order to determine the effectiveness and efficiency when placed against various other tactical options. Experimental results validate that controlling the position of the units within a tactical situation is an effective alternative for an RTS AI agent to win a battle

BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.Comment: Accepted by The 2024 ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) (Proc. CSCW 2024