What is commonsense reasoning?
Commonsense reasoning in AI refers to the ability of an artificial intelligence system to understand, interpret, and reason about everyday situations, objects, actions, and events that are typically encountered in human experiences and interactions. This involves applying general knowledge or intuitive understanding of common sense facts, rules, and relationships to make informed judgments, predictions, or decisions based on the given context or scenario.
Developing AI systems with this capability remains difficult due to the ambiguity of natural language and the challenge of encoding everyday knowledge into machine-readable formats. Progress is typically measured with benchmarks built specifically to test this gap between pattern matching and genuine reasoning:
- Winograd Schema Challenge — Pairs of sentences that differ by a single word, requiring resolution of a pronoun's referent using world knowledge rather than syntax alone.
- COPA (Choice of Plausible Alternatives) — Given a premise, the system selects which of two alternatives is the more plausible cause or effect.
- SWAG (Situations With Adversarial Generations) — Multiple-choice questions asking a model to predict the most likely next event in a described situation.
- ATOMIC — A knowledge graph of everyday inferential knowledge (causes, effects, and mental states) used both to train and evaluate commonsense models.
Some key approaches towards enabling AI systems with commonsense reasoning capabilities include:
- Knowledge representation — Developing sophisticated data structures, ontologies, or knowledge graphs that can efficiently store, organize, and retrieve relevant commonsense facts, rules, or relationships from large-scale repositories of human knowledge (e.g., ConceptNet, WordNet).
- Statistical learning — Using machine learning algorithms and techniques to analyze and extract patterns from vast amounts of unstructured or semi-structured data (e.g., web pages, social media posts, news articles), which can help improve the system's understanding of common sense concepts, scenarios, or events.
- Symbolic reasoning — Implementing rule-based systems or logical inference engines that can reason about commonsense facts and relationships using formal symbolic representations (e.g., first-order logic, modal logic), which can help enable more robust and interpretable forms of AI decision making.
- Transfer learning and multitasking — Leveraging pre-trained models or architectures that have been fine-tuned on related tasks or domains (e.g., image classification, language translation), which can help improve the system's ability to generalize and adapt its understanding of common sense concepts or scenarios across different contexts or applications.
- Explainability and interpretability — Developing AI systems that can provide clear and human-readable explanations for their reasoning processes or decision-making outcomes, which can help build trust and foster better collaboration between humans and machines in various real-world scenarios.