Real-Time Cognitive Processing in Soar
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Anand Nayyar · 2026
Soar requires a system that is required to adapt, learn and act dynamically, and is thus an ultimate challenge in AI—real-time cognitive processing. Cognitive demands placed upon embodied agents are complex, and this chapter addresses how the Soar cognitive architecture meets these demands, providing a unified framework for perception, reasoning, decision-making, and learning. Soar’s roots in symbolic reasoning, paired with its procedural and episodic memory structures, allow agents to interpret continuous inputs, treat goals as temporal priorities and take actions in real time. At the heart of this capability is Soar’s decision cycle, which rotates through cycles of elaboration (knowledge retrieval), followed by (and instantiated in) decision (action selection), enabling the responsiveness to shifting contexts. The learning mechanism of Soarchunking, which creates reusable rules of experience from a static point, is emphasized in the chapter, which serves to address the same things more effectively in the future. Furthermore, it investigates Soar’s coupling with external worlds through sensory-motor interfaces, enabling its use in applications ranging from robotics, through autonomous systems, to interactive simulation. Soar’s strengths in balancing reactivity and deliberation are emphasized, while the chapter also addresses challenges, including dealing with computational complexity and temporal uncertainty. Domain-specific case studies highlight Soar’s application in real-time strategy games, robotic navigation, and more. The chapter concludes with a discussion of future research directions, such as enhancing temporal reasoning, hybridizing symbolic and sub-symbolic methods, and addressing scalability concerns. Connecting cognitive science and AI, this chapter demonstrates the impact of Soar on both the theoretical understanding of human cognition and the development of intelligent systems.