Commonsense reasoning is the branch of Artificial intelligence concerned with simulating the human ability to make deductions about the kind of ordinary situations they encounter every day. This includes judgements about the physical properties, purpose, intentions and possible behavior of all ordinary things, such as people, cups, water, blocks, clouds, animals, and so on. A machine which exhibits commonsense reasoning will be capable of drawing conclusions that are similar to human's folk psychology (the innate human ability to reason about other people's intentions and mental states) or naive physics (the understanding of the physical world which every normal human child exhibits).
There are several components to this problem, including:
- Developing adequately broad and deep commonsense knowledge bases.
- Developing reasoning methods that exhibit the features of human thinking, including the ability to:
- reason with knowledge that is true by default
- reason rapidly across a broad range of domains
- tolerate uncertainty in your knowledge
- take decisions under incomplete knowledge and perhaps revise that belief or decision when complete knowledge becomes available.
- Developing new kinds of cognitive architectures that support multiple reasoning methods and representations.
Prominent Researchers and Individuals Involved
- Case-based reasoning
- Frame problem
- Open Mind Common Sense
- Computational cognition
- Conceptual dependency theory
- Computational learning theory
- Decision Theory
- Inductive logic programming
- Formal concept analysis
- Rule induction
- Statistical relational learning
- Minsky, Marvin (1988). The Society of Mind.
- Davis, Ernest (1990). Representations of Commonsense Knowledge. San Mateo, CA: Morgan Kaufmann. ISBN 1-55860-033-7.
- McCarthy, John (1990). Formalizing Common Sense. Norwood, NJ: Ablex. ISBN 1-871516-49-8.
- Minsky, Marvin (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York: Simon & Schuster. ISBN 0-7432-7663-9.
- Mueller, Erik T. (2006) Commonsense Reasoning. San Francisco: Morgan Kaufmann. ISBN 0-12-369388-8.
- Commonsense Reasoning Web Site
- Commonsense Reasoning Problem Page
- Media Lab Commonsense Computing Initiative
- The Epilog project at the University of Rochester
- Review of Commonsense Reasoning
- Knowledge Infusion: In Pursuit of Robustness in Artiﬁcial Intelligence
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