Logic for Artificial Intelligence
“Logic has both seductive advantages and bothersome disadvantages.”
Patrick Winston, Artificial Intelligence, pp 283
Logic in artificial intelligence can be used to help an agent create rules of inference. It provides a formal framework for creating if-then statements. Formal logic statements can be difficult for beginners because of the symbols and vocabulary used. Below is a cheat sheet for some of the basic symbols and definitions.
| Symbol |
| ∧ |
| ∨ |
| ∀ |
| ∃ |
| ¬ |
| Word |
| Conjunction |
| Conjuct |
| Disjunction |
| Disjunct |
| Predicates |
| modus ponus |
| monotonic |
Logic focuses on using knowledge in a provable and correct way. When it is used in AI it does not prove out that the claims are true. If an agent is taught that all birds can fly, it will be able to use logic to infer that a dog is not a bird. However, it will run into problems when classifying a penguin.
It is important to keep in mind that logic is a weak representation of certain kinds of knowledge. The difference between water and ice is an example of knowledge that would be difficult to represent using logic. Determining how good a “deal” is would also be better suited to a different knowledge representation. If dealing with a change of state or ranking options, using a different knowledge system would be more appropriate.