Applying Goal-Driven Autonomy to StarCraft

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.

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).

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.

AI Magazine

This article reviews the last two IEEE Conference on Computational Intelligence and Games (CIG) StarCraft Artificial Intelligence (AI) Competitions organized by the authors; these were the fourth and fifth in a series of annual competitions initiated in 2011. StarCraft AI Competitions have been hosted in conjunction with three different events: the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), CIG, and Student StarCraft AI Tournament (SSCAIT). The purpose of these competitions is to design bots that are able to autonomously and successfully play the StarCraft game by implementing real-time strategies. Recent results reveal the promising use of AI techniques in creating successful AI entries, but there is room for improvement with respect to the bots’ ability to adapt and learn to defeat humans and scripted AI bots.

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.

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.

Nature, 2019

2006

Real-time strategy (RTS) games are complex decision domains which require quick reactions as well as strategic planning. In this paper we describe the first RTS game AI tournament, which was held in June 2006, and the programs that participated.

2010

Modern complex games and simulations pose many challenges for an intelligent agent, including partial observability, continuous time and effects, hostile opponents, and exogenous events. We present ARTUE (Autonomous Response to Unexpected Events), a domain-independent autonomous agent that dynamically reasons about what goals to pursue in response to unexpected circumstances in these types of environments. ARTUE integrates AI research in planning, environment monitoring, explanation, goal generation, and goal management. To explain our conceptualization of the problem ARTUE addresses, we present a new conceptual framework, goal-driven autonomy, for agents that reason about their goals. We evaluate ARTUE on scenarios in the TAO Sandbox, a Navy training simulation, and demonstrate its novel architecture, which includes components for Hierarchical Task Network planning, explanation, and goal management. Our evaluation shows that ARTUE can perform well in a complex environment and that each component is necessary and contributes to the performance of the integrated system.

This paper presents a competitive combat strategy and tactics in RTS Games AI. To put it simply, if a player is building up base, he is losing out on creating an army and If he is building up his army, he is losing out on having a strong base. The key to winning, in StarCraft or any other RTS game is to balance strategy, tactics, macro and micro. To improve the game, one has to be able to keep track of everything that's going on over the entire map. And one must be able to give orders quickly and efficiently so in this paper we propose a competitive battle strategy with the help of a plot and decision tree. We simulate the strategy in MicroRTS developed in java EE by conducting a game-play between human player and MicroRTS AI (Game AI), though our proposed strategy out-performs the Game AI rarely as we did not account game playing-speed that makes a huge difference in victory but at least we succeeded in introducing a strategy that could well compete the Game AI and may defeat it but rarely.