The situated approach is a philosophy in artificial intelligence (AI) and cognitive science that emphasizes the importance of context, environment, and real-world interactions in shaping intelligent behavior. Rather than seeing intelligence as something that happens only inside a computer’s ‘brain’ or through processing abstract symbols, the situated approach argues that intelligence emerges from an agent’s direct engagement with its surroundings.
This perspective contrasts with traditional symbolic AI, which often assumes that intelligent behavior can be achieved through reasoning over internal representations without any direct sensory or physical connection to the world. In the situated approach, perception, action, and environment are deeply interwoven. An agent’s decisions are not just about following programmed rules or analyzing data in the abstract. Instead, they are shaped dynamically by what the agent senses, how it moves, and the immediate feedback it receives from its environment.
Robotics is a classic area where the situated approach thrives. Imagine a robot vacuum cleaner: it doesn’t have a perfect map of your house or a pre-written list of rules for every possible scenario. Instead, it responds to obstacles, navigates around furniture, and adapts its cleaning path in real time based on what it senses. Its “intelligence” comes from this constant, situated interaction with its environment, rather than from solving complex symbolic puzzles in isolation.
The situated approach has also influenced the design of intelligent agents and interactive systems. For example, digital assistants that use sensors to detect user presence or context can adapt their responses more effectively. In reinforcement learning, agents learn by interacting with environments, receiving rewards or penalties based on their actions, and updating their strategies accordingly. This process aligns closely with the situated perspective, highlighting the value of real-world feedback loops.
One of the key insights from the situated approach is that context matters. The same action might be intelligent in one situation but unhelpful or even harmful in another. By grounding AI systems in their actual use cases and environments, designers can create more robust, flexible, and adaptive agents. This approach often involves using sensors, actuators, and real-time data to keep AI systems closely connected to the world they operate in.
Critics of purely symbolic or “disembodied” AI argue that many tasks—especially those involving perception, movement, or social interaction—cannot be adequately addressed without considering how agents are situated. The situated approach is also relevant for understanding how humans and animals learn and make decisions, suggesting that intelligence in nature is always embedded in a specific context.
Overall, the situated approach has helped broaden the understanding of intelligence in both machines and biological systems. It continues to inspire research in robotics, interactive AI, cognitive science, and beyond, encouraging a holistic view where mind, body, and environment all play essential roles in shaping smart behavior.
