This episode of Techsplainers explores model-based reflex agents, a type of AI that makes decisions using both current input and an internal model of its environment. Unlike simple reflex agents that only react to immediate stimuli, model-based agents maintain memory of past perceptions and can predict how their actions might affect their surroundings. We examine the four key components—sensors, internal model, reasoning component, and actuators—and the four-stage behavioral loop these agents follow: sensing, internal modeling, decision-making, and action. The discussion highlights use cases in autonomous vehicles, robotics, gaming, and enterprise automation, while comparing them with other agent types including goal-based, utility-based, learning, and hierarchical agents. Finally, we address the limitations of model-based reflex agents, from computational requirements to their inability to adapt their rulesets over time.
Find more information at https://www.ibm.com/think/podcasts/techsplainers
Narrated by Matt Finio
