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Diagrammatic Natural Deduction

Diagrammatic Natural Deduction. Heterogeneous Representations. People use both diagrams and text when they solve problems Any intelligent system must do the same Some problems are more efficiently solved with diagrams, others with text

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Diagrammatic Natural Deduction

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  1. Diagrammatic Natural Deduction

  2. Heterogeneous Representations • People use both diagrams and text when they solve problems • Any intelligent system must do the same • Some problems are more efficiently solved with diagrams, others with text • So we need a hybrid framework where we can combine the two

  3. What we want A framework that will allow us to: • Represent arbitrary diagrams • Represent arbitrary text • Derive new textual information from diagrams • Derive new diagrams from text and previous diagrams

  4. DNDL • A new framework for heterogeneous problem-solving • Formal syntax and semantics based on attribute structures and assumption bases • Arbitrary diagrams captured as system states • Users can reason with incomplete knowledge: three-valued logic

  5. Simple example 1: Thinning {4,5,6}:28 5:45 t2 t1 t1after t2 6:28 t1

  6. Simple example 2: Observe 6:28 5:45 t2 t1 | t1 – t2 | = 43 minutes

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