Real Time Strategy Language
Roy Hayes, Peter Beling, William Scherer

TL;DR
This paper introduces a comprehensive RTS language aimed at developing general AI agents capable of playing any RTS game, promoting research beyond game-specific solutions and towards more adaptable AI systems.
Contribution
The paper presents a full RTS language designed to enable the creation of general RTS agents that do not rely on game-specific knowledge.
Findings
Defines the full RTS language for AI development
Facilitates research on generalizable RTS AI agents
Supports development of AI that can learn and adapt across games
Abstract
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 researcher. AI researchers seek 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. The paper presents the full 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 games, defined in the RTS language. This prevents game specific knowledge from being hard coded into the system, thereby facilitating research that addresses the fundamental…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge · Constraint Satisfaction and Optimization
