What is commonsense reasoning in AI?

Commonsense reasoning in AI refers to a system’s ability to make logical inferences using everyday knowledge that humans typically acquire through experience. Unlike task-specific AI (like playing chess or translating text), it involves understanding unspoken rules about the physical world, social norms, and cause-effect relationships. For example, knowing that “you can’t carry a sofa in a backpack” or “if it’s raining, people might use umbrellas” requires commonsense reasoning. This capability is critical for AI to interact naturally in real-world scenarios, where explicit instructions or labeled data are insufficient.

The challenge lies in encoding implicit knowledge that humans rarely articulate. While modern AI models excel at pattern recognition (e.g., identifying cats in images), they often lack basic reasoning. For instance, a language model might generate a sentence like “John put the pizza in the oven, then took a nap for 3 hours,” without recognizing that the pizza would burn. Similarly, a robot instructed to “bring the milk” might not infer that the milk is likely in the fridge, or that it should check expiration dates. Current approaches attempt to address this by integrating structured knowledge bases (e.g., ConceptNet), symbolic logic, or training models on broader context, but these methods remain incomplete. Unlike humans, AI systems struggle with contextual adaptability—for example, understanding that “cold” can mean temperature, an illness, or a personality trait depending on context.

Developers are exploring hybrid architectures to bridge this gap. For example, combining neural networks with rule-based systems allows models to reference predefined commonsense rules while learning from data. In robotics, systems might use physics simulators to “understand” that pushing a glass off a table will break it. Projects like OpenAI’s GPT-4 or Google’s PaLM attempt to implicitly capture commonsense through vast training data, but they still fail in edge cases. A notable test is the Winograd Schema Challenge, where AI must resolve ambiguous pronouns (e.g., "The trophy didn’t fit in the suitcase because it was too small"—does “it” refer to the trophy or suitcase?). Solving such problems requires integrating spatial reasoning and object properties, areas where AI still lags. Progress here would enable more reliable chatbots, safer autonomous systems, and AI that can handle unscripted real-world tasks.