![]() More expressive logic than propositional – Can draw up truth tables to work out the truth of statements “P implies Q” is true if P and Q are true or if P is false E.g., “P and Q” is true if and only if P is true and Q is true Need to know how connectives affect truth – How to work out the truth of a sentence – Connectives: and, or, not, implies, equivalent – Propositions such as P meaning “it is wet” – This sentence happens to be false (there is a counterexample) – And we can understand the meaning (semantics) Example: “All lecturers are six foot tall” – i.e., what the meaning of a sentence is – How we interpret (read) sentences in the logic – How we are allowed to write down those symbols – Which symbols we can use (English: letters, punctuation) – How we can construct legal sentences in the logic – “Sherlock Holmes used pure logic to solve that…” Not to be confused with logical reasoning We are able to translate into the logical language – How much of natural language (e.g., English) – Many ways to translate from one language to another Without errors in communication (or at least, fewer) – In order to give information to agents, and get info Lay down some concrete communication rules – What kind of general representations schemes are there? – Neural networks learning (neural networks) – Inductive logic programming (logic programs) – First order theorem proving (first order logic) – Or a requirement of the programming language (e.g., Prolog) – Often it is an obvious requirement of the technique – The “best” representation has already been worked out Still a lot of work done on representation issues – Cannot have intelligence without knowledge Think about knowledge, rather than data in AI Representation Representation Representation Knowledge Representation in Artificial intelligence ![]()
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