A Review of Real‐Time Strategy Game AI (original) (raw)

Call for AI research in RTS games

Proceedings of the AAAI-04 Workshop on Challenges …, 2004

This position paper discusses AI challenges in the area of real-time strategy games and presents a research agenda aimed at improving AI performance in these popular multiplayer computer games.

Artificial intelligence design for real-time strategy games

2011

For now over a decade, real-time strategy (RTS) games have been challenging intelligence, human and artificial (AI) alike, as one of the top genre in terms of overall complexity. RTS is a prime example problem featuring multiple interacting imperfect decision makers. Elaborate dynamics, partial observability, as well as a rapidly diverging action space render rational decision making somehow elusive. Humans deal with the complexity using several abstraction layers, taking decisions on different abstract levels. Current agents, on the other hand, remain largely scripted and exhibit static behavior, leaving them extremely vulnerable to flaw abuse and no match against human players. In this paper, we propose to mimic the abstraction mechanisms used by human players for designing AI for RTS games. A non-learning agent for StarCraft showing promising performance is proposed, and several research directions towards the integration of learning mechanisms are discussed at the end of the paper.

StarCraft AI Competition : A Step Toward Human-Level AI for Real-Time Strategy Games

2016

102 AI MAGAZINE StarCraft by Blizzard Entertainment is one of the most popular and famous real-time strategy (RTS) games. It provides a dynamic environment in which several agents interact to build military units with which to fight against an opponent. This game involves three distinct “races” (Protoss, Terran, and Zerg) who build different types of units and buildings and who have particular disadvantages and strengths. Players require a great deal of economic and military power to defeat their opponents while surviving and incurring minimal damage. RTS games differ from traditional board games in that they involve simultaneous movement in real time within partially observable and nondeterministic complex environments. Since the introduction of BroodWar API, StarCraft has been an important AI research platform for the development of game-playing bots using various approaches to handle a number of units and buildings by employing careful resource management and high-level tactics. ...

RTS games as test-bed for real-time AI research

This article motivates AI research in the area of real–time strategy (RTS) games and de-scribes the road–map and the current status of the ORTS project whose goals are to imple-ment an RTS game programming environment and to build AI systems that eventually can outperform human experts in this popular and challenging domain.

New Generation of Artificial Intelligence for Real-Time Strategy Games

Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability

Artificial intelligence in computer games is still well behind academic artificial intelligence research. The computer power and memory resources have increased exponentially over the last few years and improved game artificial intelligence should not hinder the performance of the game anymore. Improvements of game artificial intelligence are necessary because an appropriate artificial intelligence for the more advanced players does not exist today. This chapter discusses artificial intelligence for real-time strategy computer games, which are ideal test beds for research on movement, tactic, and strategy. Open-source real-time strategy game development tools are presented and compared, and an enhanced combat artificial intelligence algorithm is proposed.

RTS Games and Real-Time AI Research

2004

This article 1 motivates AI research in the area of real-time strategy (RTS) games and describes the current status of the ORTS project whose goals are to implement an RTS game programming environment and to build AI systems that eventually can outperform human experts in this popular and challenging domain.

A review of computational intelligence in RTS games

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2013

Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of these challenges have applications outside the game domain. This paper provides a review on computational intelligence in real-time strategy games (RTS). It starts with challenges in real-time strategy games, then it reviews different tasks to overcome this challenges. Later, it describes the techniques used to solve this challenges and it makes a relationship between techniques and tasks. Finally, it presents a set of different frameworks used as test-beds for the techniques employed. This paper is intended to be a starting point for future researchers on this topic.

Methods and approaches analysis of artificial intelligence designing for real time strategy game

Collection "Information Technology and Security", 2021

The research provides a detailed analysis of approaches to creating AI in video games. The main area of research is AI for real-time strategies, as this genre is characterized by the complexity of the game environment and the practice of creating a comprehensive AI, consisting of several agents responsible for a particular aspect of the game. The analysis shows that the main areas of use of AI methods in strategies are strategic and tactical decisions, as well as analysis of the current situation and forecasting the enemy and his chosen strategy. Among the analyzed approaches to tactical AI, reinforcement, game tree search, Bayesian model, precedent-based solutions and neural networks are most often used. Popular approaches to building strategic AI are precedent-based decision-making, hierarchical planning, and autonomous achievement of goals. When creating a module for research and determination of plans, the most popular methods are deductive, abduction, probabilistic and precedent. In addition to the considered methods, others are used in the development, but they are not as popular as above, due to problems with speed or specific implementation, which does not allow to adapt them to the standard rules of genre games. Comparison of algorithms and implementations of AI in the framework of commercial and scientific developments. Among the main differences are the high cost of commercial development of complex agents, as well as the specifics of the scientific approach, which aims to create the most effective agent in terms of game quality, rather than maximizing positive impressions of players, which is the basis of commercial development. The reasons for insufficiently active development of scientific research in the field of AI for games in general and the genre of real-time strategies in particular are described.

Building human-level ai for real-time strategy games

2011

Video games are complex simulation environments with many real-world properties that need to be addressed in order to build robust intelligence. In particular, realtime strategy games provide a multi-scale challenge which requires both deliberative and reactive reasoning processes. Experts approach this task by studying a corpus of games, building models for anticipating opponent actions, and practicing within the game environment. We motivate the need for integrating heterogeneous approaches by enumerating a range of competencies involved in gameplay and discuss how they are being implemented in EISBot, a reactive planning agent that we have applied to the task of playing real-time strategy games at the same granularity as humans.

An Improved Dataset and Extraction Process for Starcraft AI

In order to experiment with machine learning and data mining techniques in the domain of Real-Time Strategy games such as StarCraft, a dataset is required that captures the complex detail of the interactions taking place between the players and the game. This paper describes a new extraction process by which game data is extracted both directly from game log (replay) files, and indirectly through simulating the replays within the StarCraft game engine. Data is then stored in a compact, hierarchical, and easily accessible format. This process is applied to a collection of expert replays, creating a new standardised dataset. The data recorded is enough for almost the complete game state to be reconstructed, from either player's viewpoint, at any point in time (to the nearest second). This process has revealed issues in some of the source replay files, as well as discrepancies in prior datasets. Where practical, these errors have been removed in order to produce a higher-quality reusable dataset. 1 Introduction Games are an ideal domain for exploring the capabilities of Artificial Intelligence (AI) within a constrained environment and a fixed set of rules, where problem-solving techniques can be developed and evaluated before being applied to more complex real-world problems (Schaeffer 2001). Ideally, increasingly realistic games will also lead to more human-like AI being developed (Laird and van Lent 2001). Board game AI has historically received a lot of academic and public attention, but over the past decade there has been increasing interest in research based on video game AI. Real-Time Strategy (RTS) is a genre of video games in which players indirectly control many units in a simplified military simulation, which usually includes gathering resources, building infrastructure and armies, and managing units in battle. RTS games present some of the toughest challenges for AI agents, making it a difficult area for developing competent AI (Buro and Furtak 2004). It is a particularly attractive area for AI research because of how human players can quickly become adept at dealing with the complexity of the game, with experienced humans outplaying even the best academic agents (Buro and Churchill 2012).