AI and Wargaming (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.

Steps toward Building of a Good AI for Complex Wargame-Type Simulation Games

2002

One of the key areas for the application of Artificial Intelligence to the game domain is in the design of challenging artificial opponents for human players. Complex simulations such as historical wargames can be seen as natural extensions of classical games where AI techniques such as planning or learning have already proved powerful. Yet the parallel nature of more recent games introduce new levels of complexity which can be tackled at various levels. This paper focuses on the question of finding good representations for the AI design, which implies finding relevant granularities for the various tasks involved, for a popular historical wargame. This work is based on the partially automated use of the rules of the game, as well as some common sense and historical military knowledge, to design relevant heuristics. The resulting gain in representation complexity will help the application of techniques such as Reinforcement Learning.

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.

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.

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.

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).

A Review of Real‐Time Strategy Game AI

AI Magazine, 2014

This literature review covers AI techniques used for real‐time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academe and industry, finding the academic research heavily focused on creating game‐winning agents, while the industry aims to maximize player enjoyment. It finds that industry adoption of academic research is low because it is either inapplicable or too time‐consuming and risky to implement in a new game, which highlights an area for potential investigation: bridging the gap between academe and industry. Finally, the areas of spatial reasoning, multiscale AI, and cooperation are found to require future work, and standardized evaluation methods are proposed to produce comparable results between studies.

An experiment in tactical wargaming with platforms enabled by artificial intelligence

The Journal of Defense Modeling and Simulation, 2022

T he U.S. Department of Defense (DoD) has initiated several efforts to accelerate development and fielding of warfighting capabilities incorporating artificial intelligence and machine learning (AI/ML) (Department of Defense Directive 3000.09, 2017). For example, the Secretary of Defense established the Joint Artificial Intelligence Center (under the Chief Information Officer) as a DoD-wide center of excellence for AI/ML, and each of the services has established an artificial intelligence (AI) Task Force or Cross-Functional Team. DoD's leadership views fielding AI/ML capabilities as a high priority. The development and fielding of AI/ML capabilities requires the cooperation of operators and engineers. Wargames and tabletop exercises are ways to engage these two communities to enhance their understanding of AI/ML technologies, the capabilities they enable, and the considerations inherent in their adoption. Wargames and tabletop exercises also help operators and engineers develop realizable requirements and engineering specifications. This report describes an experiment conducted by RAND Corporation researchers exploring the realistic incorporation of AI/ML-enabled capabilities in company-level tabletop wargames set in the Baltic states in the 2030s between Blue (U.S.) and Red (Russian) forces. Two wargames were conducted: (1) a baseline game with remotely

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.

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. ...