Real Time Strategy Language
This addresses the need for AI researchers to develop general agents that can interpret, learn, and apply knowledge across multiple RTS games, moving beyond incremental game-specific optimizations.
The paper tackles the problem of AI systems being limited to playing one specific Real Time Strategy (RTS) game by introducing a full RTS language to enable the development of general AI agents that can play any RTS game, thereby shifting research focus towards more autonomous and knowledge-applying AI.
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 concerns of artificial intelligence.