What is commonsense knowledge?

What is commonsense knowledge?

Commonsense knowledge refers to the basic, self-evident knowledge that most people possess about the world around them. This includes understanding of everyday objects, events, and situations, as well as the ability to make sense of and interact with the world. Examples of commonsense knowledge include knowing that you should not enter an elevator until others have exited, or that if you stick a pin into a carrot, it makes a hole in the carrot, not the pin.

In the context of artificial intelligence (AI), commonsense knowledge refers to the ability of a computer system to understand and process this type of knowledge. However, representing commonsense knowledge in AI is a challenge because it is often informal and hard to formalize. Some methods for acquiring commonsense knowledge in AI include reading and extracting information from texts using natural language processing techniques, and observing and learning from human behavior.

Commonsense knowledge is different from encyclopedic knowledge as it deals with general knowledge about the world, rather than specific facts about particular subjects. It is considered "real world knowledge" that can provide a basis for additional knowledge.

Commonsense knowledge is crucial for AI systems to behave intelligently in unexpected situations. Despite many efforts, progress in this area has been slow due to the difficulty in formalizing such knowledge and the lack of a precise definition of what constitutes completeness in commonsense knowledge.

How can commonsense knowledge be represented in AI systems?

Commonsense knowledge in AI systems can be represented through several approaches:

1. Knowledge Representation Languages — Languages like RDF (Resource Description Framework) or OWL (Web Ontology Language) enable the formal definition of concepts and their interrelations in a machine-readable format.

2. Commonsense Knowledge Bases — Databases such as ConceptNet provide a structured collection of commonsense knowledge that can be queried flexibly, although they may present challenges for certain tasks.

3. Visually Grounded Knowledge Acquisition — Vision-language models can be used to extract commonsense knowledge from visual data, summarizing relations from images.

4. Symbolic and Neural Knowledge Representations — A blend of symbolic (discrete) and neural (continuous vector-like) representations can be used to support human-like reasoning and question answering.

Each of these methods has its own strengths and limitations, and the best approach for a given AI system may involve a combination of these techniques, tailored to the specific needs and capabilities of the system.

What are some techniques for acquiring commonsense knowledge in AI systems?

Acquiring commonsense knowledge in AI systems can be achieved through several techniques:

1. Reading and Extracting Information from Texts — AI systems can read and extract information from various texts to acquire commonsense knowledge. This process often involves natural language processing techniques to understand and interpret the information.

2. Interactive Systems — AI systems can use interactive systems like chatbots to ask questions to humans and acquire commonsense knowledge from their responses.

3. Building Knowledge Graphs — Knowledge graphs can be used to represent commonsense knowledge in a structured format. These graphs can then be used by AI systems to understand and reason about the world.

4. Using Knowledge from Expert Systems — AI systems can use pre-existing knowledge from expert systems or humans to learn and acquire commonsense knowledge.

5. Crowdsourcing — Some AI systems use crowdsourcing approaches to construct a knowledge base by combining the collective knowledge and participation of many non-expert people.

6. Automated Construction of Commonsense Knowledge Bases (CSKBs) — There have been efforts to automate the creation of CSKBs, building upon earlier expert-driven initiatives like CYC and WordNet.

7. Language Models and Question Answering — Language models can be trained to understand and generate text that exhibits commonsense reasoning. These models can be evaluated through tasks like question answering.

Each of these techniques has its own strengths and limitations, and the best approach for a given AI system may involve a combination of these techniques, tailored to the specific needs and capabilities of the system.

What are some limitations of using knowledge bases for commonsense knowledge?

Knowledge bases face several challenges. They are inherently incomplete, as the breadth of commonsense knowledge is vast and ever-growing, which can lead to gaps in the information available to a system. Capturing the nuanced and variable context in which commonsense facts apply is difficult, and formalizing this informal, implicit knowledge presents significant challenges.

The scalability of knowledge bases is another issue; as information accumulates, it becomes harder to manage and keep current. Ambiguities and vagueness in commonsense statements can lead to multiple valid interpretations, and the dynamic nature of knowledge means that bases can quickly become outdated without continuous updates to reflect new information and societal shifts.

Furthermore, many knowledge bases rely on language, which is inherently ambiguous and context-dependent, leading to potential misinterpretation by AI systems. Integrating these knowledge bases with other AI systems, such as machine learning models, raises technical challenges, and the creation and maintenance of large-scale knowledge bases demand substantial computational resources.

For how AI systems apply commonsense knowledge to draw inferences and make decisions, see commonsense reasoning.