SORTS: A Human-Level Approach to Real-Time Strategy 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.

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.

Proceedings of the Winter Simulation Conference 2014, 2014

Real Time Strategy (RTS) games provide complex domain to test the latest artificial intelligence (AI) research. In much of the literature, AI systems have been limited to playing one game. Although, this specialization has resulted in stronger AI gaming systems it does not address the key concerns of AI research, which focuses on the development of AI agents that can autonomously interpret, learn, and apply new knowledge. To achieve human level performance, current AI systems rely on game specific knowledge of an expert. This paper proposes a RTS language in hopes of shifting the current research focus to the development of general RTS agents. General RTS agents are AI gaming systems that can play any RTS game, defined in the proposed RTS language. The structure of the RTS language prevents game specific knowledge from being hard coded into the system, thereby facilitating research that addresses the fundamental concerns of artificial intelligence.

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. ...

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.

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.

2005

In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using

2020

This paper introduces Tribes, a new turn-based strategy game framework. Tribes is a multi-player, multi-agent, stochastic and partially observable game that involves strategic and tactical combat decisions. A good playing strategy requires the management of a technology tree, build orders and economy. The framework provides a Forward Model, which can be used by Statistical Forward Planning methods. This paper describes the framework and the opportunities for Game AI research it brings. We further provide an analysis on the action space of this game, as well as benchmarking a series of agents (rule based, one step look-ahead, Monte Carlo, Monte Carlo Tree Search, and Rolling Horizon Evolution) to study their relative playing strength. Results show that although some of these agents can play at a decent level, they are still far from human playing strength.

Foundations of Digital Games 2013

Design is an essential part of all games and narratives, yet designing and implementing believable game strategies can be time consuming and error-prone. This motivates the development and application of systems AI methodologies. Here we demonstrate for the first time the iterative development of agent behaviour for a real-time strategy game (here StarCraft) utilising Behaviour Oriented Design (BOD). BOD provides focus on the robust creation and easy adjustment of modular and hierarchical cognitive agents. We demonstrate BOD's usage in creating an AI capable of playing the StarCraft character the Zerg hive mind, and document its performance against a variety of opponent AI systems. In describing our tool-driven development process, we also describe the new Abode IDE, provide a brief literature review situating BOD in the AI game literature, and propose possible future work.